Repository: liunian-Jay/Awesome-RAG Branch: main Commit: d060ca9102f3 Files: 1 Total size: 95.6 KB Directory structure: gitextract_blcxf717/ └── README.md ================================================ FILE CONTENTS ================================================ ================================================ FILE: README.md ================================================ <div align="center"> <!-- <p align="center"> --> <h1 align="center"><strong>Awesome-RAG</strong></h1> </div> <div align="center">   </div> 💡 List of recent developments in Retrieval-Augmented Generation (RAG) for large language models (LLM). 🤗 We welcome and encourage researchers to submit pull requests to update information in their papers! 📫 _Repo under active development. Collaborations welcome on **Framework** & **Survey**. Contact: jiangyijcx@163.com._ ## 📕 Overview ### [📌 Accepted papers](#Accept) <a name="Accept"></a> <small> | | | | | | |---------------------------|----------------------------|------------------------------|--------------------------|---------------------------| | NIPS 2025 | EMNLP 2025 | [ACL 2025](#ACL-2025) | [ICML 2025](#ICML-2025) | [ICLR 2025](#ICLR-2025) | | [NIPS 2024](#NIPS-2024) | [EMNLP 2024](#EMNLP-2024) | [ACL 2024](#ACL-2024) | [ICML 2024](#ICML-2024) | [ICLR 2024](#ICLR-2024) | </small> ### [🗓️ 2026 papers](#2026) <a name="2026"></a> | | | | | | | |---------------------------------|---------------------------------|---------------------------------|---------------------------------|---------------------------------|---------------------------------| | 2026.06 | 2026.05 | 2026.04 | [2026.03](#2026-March) | [2026.02](#2026-February) | [2026.01](#2026-January) | ### [🗓️ 2025 papers](#2025) <a name="2025"></a> | | | | | | | |---------------------------------|---------------------------------|---------------------------------|---------------------------------|---------------------------------|---------------------------------| | [2025.12](#2025-December) | [2025.11](#2025-November) | [2025.10](#2025-October) | [2025.09](#2025-September) | [2025.08](#2025-August) |[2025.07](#2025-July) | | [2025.06](#2025-June) | [2025.05](#2025-May) | [2025.04](#2025-April) | [2025.03](#2025-March) | [2025.02](#2025-February) | [2025.01](#2025-January) | ### [🗓️ 2024 papers](#2024) <a name="2024"></a> | | | | | | | |---------------------------------|---------------------------------|---------------------------------|-----------------------------------|-----------------------------------|-----------------------------------| | [2024.12](#2024-December) | [2024.11](#2024-November) | [2024.10](#2024-October) | [2024.09](#2024-September) | [2024.08](#2024-August) |[2024.07](#2024-July) | | [2024.06](#2024-June) | [2024 .05](#2024-May) | [2024.04](#2024-Apri) | [2024.03](#2024-March) | [2024.02](#2024-February) | [2024.01](#2024-January) | ### 🗃️ Evaluation Datasets <a name="dataset"></a> | | | | | | | |---------------------------------|---------------------------------|---------------------------------|-----------------------------------|-----------------------------------|-----------------------------------| | [HotpotQA](https://hotpotqa.github.io/) | [2WikiMultiHopQA](https://github.com/Alab-NII/2wikimultihop) | [WebQuestions](https://nlp.stanford.edu/software/sempre/) | [TriviaQA](http://nlp.cs.washington.edu/triviaqa/) | [MuSiQue](https://github.com/stonybrooknlp/musique) |[NaturalQA](https://ai.google.com/research/NaturalQuestions) | | [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) | [PopQA](https://github.com/AlexTMallen/adaptive-retrieval) | [ASQA](https://github.com/google-research/language/tree/master/language/asqa) | [Bamboogle](https://huggingface.co/datasets/chiayewken/bamboogle) | [ARC_Challenge](http://data.allenai.org/arc) | [PubHealth](https://github.com/luohongyin/unilc) | ## 📢 Latest News - **[26.04]:** Our **CoCoA** accepted at ***ACL2026 Main***! 🎉 [[Paper]](https://arxiv.org/pdf/2508.01696)[[Code]](https://github.com/liunian-Jay/CoCoA) - **[26.1]:** Our [ArcAligner](https://arxiv.org/pdf/2601.05038) released — designed for long memory!🚀 [[Code]](https://github.com/liunian-Jay/ArcAligner) - **[26.1]:** Our [OptiSet](https://arxiv.org/pdf/2601.05027) released — unified selection and ranking!🚀 [[Code]](https://github.com/liunian-Jay/OptiSet) - **[25.10]:** Updated the recent papers from September and October!📅 - **[25.10]:** Our [QAgent](https://arxiv.org/pdf/2510.08383) released — an agentic RAG framework!🚀 [[Code]](https://github.com/LivingFutureLab/QAgent) - **[25.08]:** Our [CoCoA](https://arxiv.org/pdf/2508.01696) released — studying knowledge synergy!🚀 [[Code]](https://github.com/liunian-Jay/CoCoA) - **[25.06]:** We built [AgenticRAG-RL](https://github.com/liunian-Jay/AgenticRAG-RL) — a minimal RL-RAG! Feel free to contribute!🤝 - **[25.05]:** Our [GainRAG](https://arxiv.org/pdf/2505.18710) released — studying preference alignment!🚀 [[Code]](https://github.com/liunian-Jay/GainRAG) - **[25.05]:** Our **GainRAG** accepted at ***ACL2025 Main***! 🎉 [[Paper]](https://arxiv.org/pdf/2505.18710)[[Code]](https://github.com/liunian-Jay/GainRAG) - **[25.01-05]:** Updated the papers from 2025! 📄 - **[24.10]:** We built [MU-GOT](https://github.com/liunian-Jay/MU-GOT) — a PDF parsing tool! Feel free to contribute!🤝 - **[24.06-12]:** Updated the papers from 2024! 📄 ### 🎁 Resources #### 💡Survey - [2025.05] A Survey on Knowledge-Oriented Retrieval-Augmented Generation [[Link]](https://arxiv.org/pdf/2503.10677) - [2025.01] Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG [[Link]](https://arxiv.org/pdf/2501.09136) - [2024.09] Trustworthiness in Retrieval-Augmented Generation Systems: A Survey [[Link]](https://arxiv.org/pdf/2409.10102?) - [2024.09] Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely [[Link]](https://arxiv.org/pdf/2409.14924?) - [2024.07] Retrieval-Augmented Generation for Natural Language Processing: A Survey [[Link]](https://arxiv.org/pdf/2407.13193?) - [2024.05] A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models [[Link]](https://arxiv.org/pdf/2405.06211) - [2024.02] Retrieval-Augmented Generation for AI-Generated Content: A Survey [[Link]](https://arxiv.org/pdf/2402.19473) - [2023.12] Retrieval-Augmented Generation for Large Language Models: A Survey [[Link]](https://arxiv.org/pdf/2312.10997) #### 💡Project - [LightRAG](https://github.com/HKUDS/LightRAG) - [RAGFlow](https://github.com/infiniflow/ragflow) - [RAG-Anything](https://github.com/HKUDS/RAG-Anything) - [Awesome-LLM-RAG](https://github.com/jxzhangjhu/Awesome-LLM-RAG) ## 🔥Latest Papers ### 🔥2026 March - Mar 30 [PAR2-RAG: Planned Active Retrieval and Reasoning for Multi-Hop Question Answering](https://arxiv.org/pdf/2603.29085) - Mar 30 [Courtroom-Style Multi-Agent Debate with Progressive RAG and Role-Switching for Controversial Claim Verification](https://arxiv.org/pdf/2603.28488) - Mar 27 [Not All Entities are Created Equal: A Dynamic Anonymization Framework for Privacy-Preserving Retrieval-Augmented Generation](https://arxiv.org/pdf/2603.26074) - Mar 27 [Insider Knowledge: How Much Can RAG Systems Gain from Evaluation Secrets?](https://arxiv.org/pdf/2601.13227) - Mar 26 [Adaptive Chunking: Optimizing Chunking-Method Selection for RAG](https://arxiv.org/pdf/2603.25333) - March 26 [UniAI-GraphRAG: Synergizing Ontology-Guided Extraction, Multi-Dimensional Clustering, and Dual-Channel Fusion for Robust Multi-Hop Reasoning](https://arxiv.org/pdf/2603.25152) - Mar 26 [GroupRAG: Cognitively Inspired Group-Aware Retrieval and Reasoning via Knowledge-Driven Problem Structuring](https://arxiv.org/pdf/2603.26807) - Mar 25 [Retrieval Improvements Do Not Guarantee Better Answers: A Study of RAG for AI Policy QA](https://arxiv.org/pdf/2603.24580) - Mar 25 [CoCR-RAG: Enhancing Retrieval-Augmented Generation in Web Q&A via Concept-oriented Context Reconstruction](https://arxiv.org/pdf/2603.23989) - Mar 24 [From Conflict to Consensus: Boosting Medical Reasoning via Multi-Round Agentic RAG](https://arxiv.org/pdf/2603.03292) - Mar 23 [CatRAG: Functor-Guided Structural Debiasing with Retrieval Augmentation for Fair LLMs](https://arxiv.org/pdf/2603.21524) - Mar 21 [Graphs RAG at Scale: Beyond Retrieval-Augmented Generation With Labeled Property Graphs and Resource Description Framework for Complex and Unknown Search Spaces](https://arxiv.org/pdf/2603.22340) - Mar 19 [GIP-RAG: An Evidence-Grounded Retrieval-Augmented Framework for Interpretable Gene Interaction and Pathway Impact Analysis](https://arxiv.org/pdf/2603.20321) - Mar 19 [BubbleRAG: Evidence-Driven Retrieval-Augmented Generation for Black-Box Knowledge Graphs](https://arxiv.org/pdf/2603.20309) - Mar 19 [DaPT: A Dual-Path Framework for Multilingual Multi-hop Question Answering](https://arxiv.org/pdf/2603.19097) - Mar 18 [PACE-RAG: Patient-Aware Contextual and Evidence-based Policy RAG for Clinical Drug Recommendation](https://arxiv.org/pdf/2603.17356) - Mar 18 [SF-RAG: Structure-Fidelity Retrieval-Augmented Generation for Academic Question Answering](https://arxiv.org/pdf/2602.13647) - Mar 17 [Is Conformal Factuality for RAG-based LLMs Robust? Novel Metrics and Systematic Insights](https://arxiv.org/abs/2603.16817) - Mar 17 [IndexRAG: Bridging Facts for Cross-Document Reasoning at Index Time](https://arxiv.org/pdf/2603.16415) - Mar 16 [Cross-RAG: Zero-Shot Retrieval-Augmented Time Series Forecasting via Cross-Attention](https://arxiv.org/pdf/2603.14709) - Mar 14 [The Reasoning Bottleneck in Graph-RAG: Structured Prompting and Context Compression for Multi-Hop QA](https://arxiv.org/pdf/2603.14045) - Mar 12 [Test-Time Strategies for More Efficient and Accurate Agentic RAG](https://arxiv.org/pdf/2603.12396) - Mar 11 [RAGPerf: An End-to-End Benchmarking Framework for Retrieval-Augmented Generation Systems](https://arxiv.org/pdf/2603.10765) - Mar 10 [TaSR-RAG: Taxonomy-guided Structured Reasoning for Retrieval-Augmented Generation](https://arxiv.org/pdf/2603.09341) - Mar 9 [SPD-RAG: Sub-Agent Per Document Retrieval-Augmented Generation](https://arxiv.org/pdf/2603.08329) - Mar 8 [KohakuRAG: A simple RAG framework with hierarchical document indexing](https://arxiv.org/pdf/2603.07612) - Mar 7 [Hit-RAG: Learning to Reason with Long Contexts via Preference Alignment](https://arxiv.org/pdf/2603.07023) - Mar 6 [LIT-RAGBench: Benchmarking Generator Capabilities of Large Language Models in Retrieval-Augmented Generation](https://arxiv.org/pdf/2603.06198) - Mar 5 [Towards Robust Retrieval-Augmented Generation Based on Knowledge Graph: A Comparative Analysis](https://arxiv.org/pdf/2603.05698) - Mar 5 [MedCoRAG: Interpretable Hepatology Diagnosis via Hybrid Evidence Retrieval and Multispecialty Consensus](https://arxiv.org/pdf/2603.05129) - Mar 5 [S-Path-RAG: Semantic-Aware Shortest-Path Retrieval Augmented Generation for Multi-Hop Knowledge Graph Question Answering](https://arxiv.org/pdf/2603.23512) - Mar 3 [RAG-X: Systematic Diagnosis of Retrieval-Augmented Generation for Medical Question Answering](https://arxiv.org/pdf/2603.03541) - Mar 2 [URAG: A Benchmark for Uncertainty Quantification in Retrieval-Augmented Large Language Models ](https://arxiv.org/pdf/2603.19281) - Mar 2 [GAM-RAG: Gain-Adaptive Memory for Evolving Retrieval in Retrieval-Augmented Generation](https://arxiv.org/pdf/2603.01783) - Mar 1 [Tiny-Critic RAG: Empowering Agentic Fallback with Parameter-Efficient Small Language Models](https://arxiv.org/pdf/2603.00846) ### 🔥2026 February - Feb 28 [From Flat to Structural: Enhancing Automated Short Answer Grading with GraphRAG](https://arxiv.org/pdf/2603.19276) - Feb 26 [TCM-DiffRAG: Personalized Syndrome Differentiation Reasoning Method for Traditional Chinese Medicine based on Knowledge Graph and Chain of Thought](https://arxiv.org/pdf/2602.22828) - Feb 26 [Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG Training](https://arxiv.org/pdf/2602.22576) - Feb 25 [Revisiting RAG Retrievers: An Information Theoretic Benchmark](https://arxiv.org/pdf/2602.21553) - Feb 24 [HELP: HyperNode Expansion and Logical Path-Guided Evidence Localization for Accurate and Efficient GraphRAG](https://arxiv.org/pdf/2602.20926) - Feb 24 [RMIT-ADM+S at the MMU-RAG NeurIPS 2025 Competition](https://arxiv.org/pdf/2602.20735) - Feb 24 [DynaRAG: Bridging Static and Dynamic Knowledge in Retrieval-Augmented Generation](https://arxiv.org/pdf/2603.18012) - Feb 23 [How Retrieved Context Shapes Internal Representations in RAG](https://arxiv.org/pdf/2602.20091) - Feb 23 [Controllable Evidence Selection in Retrieval-Augmented Question Answering via Deterministic Utility Gating](https://arxiv.org/pdf/2603.18011) - Feb 22 [AgenticRAGTracer: A Hop-Aware Benchmark for Diagnosing Multi-Step Retrieval Reasoning in Agentic RAG](https://arxiv.org/pdf/2602.19127) - Feb 21 [Rethinking Retrieval-Augmented Generation as a Cooperative Decision-Making Problem](https://arxiv.org/pdf/2602.18734) - Feb 20 [GraphSkill: Documentation-Guided Hierarchical Retrieval-Augmented Coding for Complex Graph Reasoning](https://arxiv.org/pdf/2603.06620) - Feb 19 [NTLRAG: Narrative Topic Labels derived with Retrieval Augmented Generation](https://arxiv.org/pdf/2602.17216) - Feb 19 [NotebookRAG: Retrieving Multiple Notebooks to Augment the Generation of EDA Notebooks for Crowd-Wisdom](https://arxiv.org/pdf/2602.17215) - Feb 17 [Concept-Enhanced Multimodal RAG: Towards Interpretable and Accurate Radiology Report Generation](https://arxiv.org/pdf/2602.15650) - Feb 16 [AIC CTU@AVerImaTeC: dual-retriever RAG for image-text fact checking](https://arxiv.org/pdf/2602.15190) - Feb 16 [HyperRAG: Reasoning N-ary Facts over Hypergraphs for Retrieval Augmented Generation](https://arxiv.org/pdf/2602.14470) - Feb 16 [Differentially Private Retrieval-Augmented Generation](https://arxiv.org/pdf/2602.14374) - Feb 14 [Evaluating Prompt Engineering Techniques for RAG in Small Language Models: A Multi-Hop QA Approach](https://arxiv.org/pdf/2602.13890) - Feb 13 [LIR^3AG: A Lightweight Rerank Reasoning Strategy Framework for Retrieval-Augmented Generation](https://arxiv.org/abs/2512.18329) - Feb 11 [MultiCube-RAG for Multi-hop Question Answering](https://arxiv.org/pdf/2602.15898) - Feb 11 [AudioRAG: A Challenging Benchmark for Audio Reasoning and Information Retrieval](https://arxiv.org/pdf/2602.10656) - Feb 10 [MLDocRAG: Multimodal Long-Context Document Retrieval Augmented Generation](https://arxiv.org/pdf/2602.10271) - Feb 10 [Comprehensive Comparison of RAG Methods Across Multi-Domain Conversational QA](https://arxiv.org/pdf/2602.09552) - Feb 10 [Evaluating Social Bias in RAG Systems: When External Context Helps and Reasoning Hurts](https://arxiv.org/pdf/2602.09442) - Feb 9 [DA-RAG: Dynamic Attributed Community Search for Retrieval-Augmented Generation](https://arxiv.org/pdf/2602.08545) - Feb 9 [SCOUT-RAG: Scalable and Cost-Efficient Unifying Traversal for Agentic Graph-RAG over Distributed Domains](https://arxiv.org/pdf/2602.08400) - Feb 8 [HypRAG: Hyperbolic Dense Retrieval for Retrieval Augmented Generation](https://arxiv.org/pdf/2602.07739) - Feb 7 [IGMiRAG: Intuition-Guided Retrieval-Augmented Generation with Adaptive Mining of In-Depth Memor](https://arxiv.org/pdf/2602.07525) - Feb 7 [Progressive Searching for Retrieval in RAG](https://arxiv.org/pdf/2602.07297) - Feb 7 [Benchmarking Legal RAG: The Promise and Limits of AI Statutory Surveys](https://arxiv.org/pdf/2603.03300) - Feb 6 [SE-Search: Self-Evolving Search Agent via Memory and Dense Reward](https://arxiv.org/pdf/2603.03293) - Feb 5 [CompactRAG: Reducing LLM Calls and Token Overhead in Multi-Hop Question Answering](https://arxiv.org/pdf/2602.05728) - Feb 5 [Cost-Efficient RAG for Entity Matching with LLMs: A Blocking-based Exploration](https://arxiv.org/pdf/2602.05708) - Feb 5 [When Iterative RAG Beats Ideal Evidence: A Diagnostic Study in Scientific Multi-hop Question Answering](https://arxiv.org/pdf/2601.19827) - Feb 4[HugRAG: Hierarchical Causal Knowledge Graph Design for RAG](https://arxiv.org/pdf/2602.05143) - Feb 4 [Pruning Minimal Reasoning Graphs for Efficient Retrieval-Augmented Generation](https://arxiv.org/pdf/2602.04926) - Feb 4 [Atomic Information Flow: A Network Flow Model for Tool Attributions in RAG Systems](https://arxiv.org/pdf/2602.04912) - Feb 3 [LUMINA: Detecting Hallucinations in RAG System with Context-Knowledge Signals](https://arxiv.org/pdf/2509.21875) - Feb 3 [Rethinking the Reranker: Boundary-Aware Evidence Selection for Robust Retrieval-Augmented Generation](https://arxiv.org/pdf/2602.03689) - Feb 3 [Reinforcement Fine-Tuning for History-Aware Dense Retriever in RAG](https://arxiv.org/pdf/2602.03645) - Feb 3 [Use Graph When It Needs: Efficiently and Adaptively Integrating Retrieval-Augmented Generation with Graphs](https://arxiv.org/pdf/2602.03578) - Feb 3 [A-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces](https://arxiv.org/pdf/2602.03442) - Feb 3 [Pursuing Best Industrial Practices for Retrieval-Augmented Generation in the Medical Domain](https://arxiv.org/pdf/2602.03368) - Feb 2 [Breaking the Static Graph: Context-Aware Traversal for Robust Retrieval-Augmented Generation](https://arxiv.org/pdf/2602.01965) - Feb 2 [CTRL-RAG: Contrastive Likelihood Reward Based Reinforcement Learning for Context-Faithful RAG Models](https://arxiv.org/pdf/2603.04406) - Feb 2 [P-RAG: Prompt-Enhanced Parametric RAG with LoRA and Selective CoT for Biomedical and Multi-Hop QA](https://arxiv.org/pdf/2602.15874) ### 🔥2026 January - Jan 30 [Bounding Hallucinations: Information-Theoretic Guarantees for RAG Systems via Merlin-Arthur Protocols](https://arxiv.org/pdf/2512.11614) - Jan 30 [DIVERGE: Diversity-Enhanced RAG for Open-Ended Information Seeking](https://arxiv.org/pdf/2602.00238) - Jan 30 [RAGRouter-Bench: A Dataset and Benchmark for Adaptive RAG Routing](https://arxiv.org/pdf/2602.00296) - Jan 29 [ProRAG: Process-Supervised Reinforcement Learning for Retrieval-Augmented Generation](https://arxiv.org/pdf/2601.21912) - Jan 29 [EHR-RAG: Bridging Long-Horizon Structured Electronic Health Records and Large Language Models via Enhanced Retrieval-Augmented Generation](https://arxiv.org/pdf/2601.21340) - Jan 27 [LURE-RAG: Lightweight Utility-driven Reranking for Efficient RAG](https://arxiv.org/pdf/2601.19535) - Jan 27 [RPO-RAG: Aligning Small LLMs with Relation-aware Preference Optimization for Knowledge Graph Question Answering](https://arxiv.org/pdf/2601.19225) - Jan 24 [Less is More for RAG: Information Gain Pruning for Generator-Aligned Reranking and Evidence Selection](https://arxiv.org/pdf/2601.17532) - Jan 23 [DeepEra: A Deep Evidence Reranking Agent for Scientific Retrieval-Augmented Generated Question Answering](https://arxiv.org/pdf/2601.16478) - Jan 23 [DF-RAG: Query-Aware Diversity for Retrieval-Augmented Generation](https://arxiv.org/pdf/2601.17212) - Jan 22 [SPARC-RAG: Adaptive Sequential-Parallel Scaling with Context Management for Retrieval-Augmented Generation](https://arxiv.org/pdf/2602.00083) - Jan 21 [ManuRAG: Multi-modal Retrieval Augmented Generation for Manufacturing Question Answering (Early Version)](https://arxiv.org/pdf/2601.15434) - Jan 21 [MiRAGE: A Multiagent Framework for Generating Multimodal Multihop Question-Answer Dataset for RAG Evaluation](https://arxiv.org/pdf/2601.15487) - Jan 20 [Predicting Retrieval Utility and Answer Quality in Retrieval-Augmented Generation](https://arxiv.org/pdf/2601.14546) - Jan 19 [RAGExplorer: A Visual Analytics System for the Comparative Diagnosis of RAG Systems](https://arxiv.org/pdf/2601.12991) - Jan 19 [Augmenting Question Answering with A Hybrid RAG Approach](https://arxiv.org/pdf/2601.12658) - Jan 16 [NAACL: Noise-AwAre Verbal Confidence Calibration for LLMs in RAG Systems](https://arxiv.org/pdf/2601.11004) - Jan 16 [PruneRAG: Confidence-Guided Query Decomposition Trees for Efficient Retrieval-Augmented Generation](https://arxiv.org/pdf/2601.11024) - Jan 16 [Reasoning in Trees: Improving Retrieval-Augmented Generation for Multi-Hop Question Answering](https://arxiv.org/pdf/2601.11255) - Jan 16 [Unlocking the Potentials of Retrieval-Augmented Generation for Diffusion Language Models](https://arxiv.org/pdf/2601.11342) - Jan 16 [Deep GraphRAG: A Balanced Approach to Hierarchical Retrieval and Adaptive Integration](https://arxiv.org/pdf/2601.11144) - Jan 16 [Predict the Retrieval! Test time adaptation for Retrieval Augmented Generation](https://arxiv.org/pdf/2601.11443) - Jan 15 [RoutIR: Fast Serving of Retrieval Pipelines for Retrieval-Augmented Generation](https://arxiv.org/pdf/2601.10644) - Jan 13 [RAGShaper: Eliciting Sophisticated Agentic RAG Skills via Automated Data Synthesis](https://arxiv.org/pdf/2601.08699) - Jan 12 [Relink: Constructing Query-Driven Evidence Graph On-the-Fly for GraphRAG](https://arxiv.org/pdf/2601.07192) - Jan 12 [BayesRAG: Probabilistic Mutual Evidence Corroboration for Multimodal Retrieval-Augmented Generation](https://arxiv.org/pdf/2601.07329) - Jan 12 [FROAV: A Framework for RAG Observation and Agent Verification - Lowering the Barrier to LLM Agent Research](https://arxiv.org/pdf/2601.07504) - Jan 12 [Is Agentic RAG worth it? An experimental comparison of RAG approaches](https://arxiv.org/pdf/2601.07711) - Jan 11 [TreePS-RAG: Tree-based Process Supervision for Reinforcement Learning in Agentic RAG](https://arxiv.org/pdf/2601.06922) - Jan 11 [Seeing through the Conflict: Transparent Knowledge Conflict Handling in Retrieval-Augmented Generation](https://arxiv.org/pdf/2601.06842) - Jan 11 [Fine-Tuning vs. RAG for Multi-Hop Question Answering with Novel Knowledge](https://arxiv.org/pdf/2601.07054) - Jan 10 [Attribution Techniques for Mitigating Hallucinated Information in RAG Systems: A Survey](https://arxiv.org/pdf/2601.19927) - Jan 10 [MedRAGChecker: Claim-Level Verification for Biomedical Retrieval-Augmented Generation](https://arxiv.org/pdf/2601.06519) - Jan 10 [L-RAG: Balancing Context and Retrieval with Entropy-Based Lazy Loading](https://arxiv.org/pdf/2601.06551) - Jan 8 [Self-MedRAG: a Self-Reflective Hybrid Retrieval-Augmented Generation Framework for Reliable Medical Question Answering](https://arxiv.org/pdf/2601.04531) - Jan 8 [Orion-RAG: Path-Aligned Hybrid Retrieval for Graphless Data](https://arxiv.org/pdf/2601.04764) - Jan 8 [OptiSet: Unified Optimizing Set Selection and Ranking for Retrieval-Augmented Generation](https://arxiv.org/pdf/2601.05027) - Jan 8 [ArcAligner: Adaptive Recursive Aligner for Compressed Context Embeddings in RAG](https://arxiv.org/pdf/2601.05038) - Jan 6 [Enhancing Multilingual RAG Systems with Debiased Language Preference-Guided Query Fusion](https://arxiv.org/pdf/2601.02956) - Jan 6 [Stable-RAG: Mitigating Retrieval-Permutation-Induced Hallucinations in Retrieval-Augmented Generation](https://arxiv.org/pdf/2601.02993) - Jan 6 [Detecting Hallucinations in Retrieval-Augmented Generation via Semantic-level Internal Reasoning Graph](https://arxiv.org/pdf/2601.03052) - Jan 6 [Tackling the Inherent Difficulty of Noise Filtering in RAG](https://arxiv.org/pdf/2601.01896) - Jan 7 [Disco-RAG: Discourse-Aware Retrieval-Augmented Generation](https://arxiv.org/pdf/2601.04377) - Jan 5 [Clinical Knowledge Graph Construction and Evaluation with Multi-LLMs via Retrieval-Augmented Generation](https://arxiv.org/pdf/2601.01844) - Jan 4 [A Dynamic Retrieval-Augmented Generation System with Selective Memory and Remembrance](https://arxiv.org/pdf/2601.02428) - Jan 2 [Improving Multi-step RAG with Hypergraph-based Memory for Long-Context Complex Relational Modeling](https://arxiv.org/pdf/2512.23959) - Jan 2 [RAG-BioQA: A Retrieval-Augmented Generation Framework for Long-Form Biomedical Question Answering](https://arxiv.org/pdf/2510.01612) ### 🔥2025 December - Dec 31 [Enhancing Retrieval-Augmented Generation with Topic-Enriched Embeddings: A Hybrid Approach Integrating Traditional NLP Techniques](https://arxiv.org/pdf/2601.00891) - Dec 29 [Retrieval Augmented Question Answering: When Should LLMs Admit Ignorance?](https://arxiv.org/pdf/2512.23836) - Dec 27 [DICE: Discrete Interpretable Comparative Evaluation with Probabilistic Scoring for Retrieval-Augmented Generation](https://arxiv.org/pdf/2512.22629) - Dec 27 [HiFi-RAG: Hierarchical Content Filtering and Two-Pass Generation for Open-Domain RAG](https://arxiv.org/pdf/2512.22442) - Dec 25 [FVA-RAG: Falsification-Verification Alignment for Mitigating Sycophantic Hallucinations](https://arxiv.org/pdf/2512.07015) - Dec 22 [QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation](https://arxiv.org/pdf/2512.19134) - Dec 20 [Bidirectional RAG: Safe Self-Improving Retrieval-Augmented Generation Through Multi-Stage Validation](https://arxiv.org/pdf/2512.22199) - Dec 19 [MMRAG-RFT: Two-stage Reinforcement Fine-tuning for Explainable Multi-modal Retrieval-augmented Generation](https://arxiv.org/pdf/2512.17194) - Dec 17 [The Semantic Illusion: Certified Limits of Embedding-Based Hallucination Detection in RAG Systems](https://arxiv.org/pdf/2512.15068) - Dec 16 [DrugRAG: Enhancing Pharmacy LLM Performance Through A Novel Retrieval-Augmented Generation Pipeline](https://arxiv.org/pdf/2512.14896) - Dec 16 [Dynamic Context Selection for Retrieval-Augmented Generation: Mitigating Distractors and Positional Bias](https://arxiv.org/pdf/2512.14313) - Dec 16 [Cog-RAG: Cognitive-Inspired Dual-Hypergraph with Theme Alignment Retrieval-Augmented Generation](https://arxiv.org/pdf/2511.13201) - Dec 15 [Semantic Grounding Index: Geometric Bounds on Context Engagement in RAG Systems](https://arxiv.org/pdf/2512.13771) - Dec 12 [LOOPRAG: Enhancing Loop Transformation Optimization with Retrieval-Augmented Large Language Models](https://arxiv.org/pdf/2512.15766) - Dec 11 [Cooperative Retrieval-Augmented Generation for Question Answering: Mutual Information Exchange and Ranking by Contrasting Layers](https://arxiv.org/pdf/2512.10422) - Dec 10 [MedBioRAG: Semantic Search and Retrieval-Augmented Generation with Large Language Models for Medical and Biological QA](https://arxiv.org/pdf/2512.10996) - Dec 10 [RouteRAG: Efficient Retrieval-Augmented Generation from Text and Graph via Reinforcement Learning](https://arxiv.org/pdf/2512.09487) - Dec 10 [Leveraging Language Models and RAG for Efficient Knowledge Discovery in Clinical Environments](https://arxiv.org/pdf/2601.04209) - Dec 9 [Detecting Hallucinations in Graph Retrieval-Augmented Generation via Attention Patterns and Semantic Alignment](https://arxiv.org/pdf/2512.09148) - Dec 9 [Toward Faithful Retrieval-Augmented Generation with Sparse Autoencoders](https://arxiv.org/pdf/2512.08892) - Dec 5 [Optimizing Medical Question-Answering Systems: A Comparative Study of Fine-Tuned and Zero-Shot Large Language Models with RAG Framework](https://arxiv.org/pdf/2512.05863) - Dec 3 [RAGVUE: A Diagnostic View for Explainable and Automated Evaluation of Retrieval-Augmented Generation](https://arxiv.org/pdf/2601.04196) - Dec 3 [BookRAG: A Hierarchical Structure-aware Index-based Approach for Retrieval-Augmented Generation on Complex Documents](https://arxiv.org/pdf/2512.03413) ### 🔥2025 November - Nov 29 [Breaking It Down: Domain-Aware Semantic Segmentation for Retrieval Augmented Generation](https://arxiv.org/pdf/2512.00367) - Nov 28 [Autonomous QA Agent: A Retrieval-Augmented Framework for Reliable Selenium Script Generation](https://arxiv.org/pdf/2601.06034) - Nov 27 [Unlocking Electronic Health Records: A Hybrid Graph RAG Approach to Safe Clinical AI for Patient QA](https://arxiv.org/pdf/2602.00009) - Nov 26 [MegaRAG: Multimodal Knowledge Graph-Based Retrieval Augmented Generation](https://arxiv.org/pdf/2512.20626) - Nov 25 [HKRAG: Holistic Knowledge Retrieval-Augmented Generation Over Visually-Rich Documents](https://arxiv.org/pdf/2511.20227) - Nov 24 [HyperbolicRAG: Enhancing Retrieval-Augmented Generation with Hyperbolic Representations](https://arxiv.org/pdf/2511.18808) - Nov 22 [Agent-as-a-Graph: Knowledge Graph-Based Tool and Agent Retrieval for LLM Multi-Agent Systems](https://arxiv.org/pdf/2511.18194) - Nov 22 [Rethinking Retrieval: From Traditional Retrieval Augmented Generation to Agentic and Non-Vector Reasoning Systems in the Financial Domain for Large Language Models](https://arxiv.org/pdf/2511.18177) - Nov 21 [Beyond Component Strength: Synergistic Integration and Adaptive Calibration in Multi-Agent RAG Systems](https://arxiv.org/pdf/2511.21729) - Nov 20 [Comparison of Text-Based and Image-Based Retrieval in Multimodal Retrieval Augmented Generation Large Language Model Systems](https://arxiv.org/pdf/2511.16654) - Nov 19 [CARE-RAG - Clinical Assessment and Reasoning in RAG](https://arxiv.org/pdf/2511.15994) - Nov 19 [ItemRAG: Item-Based Retrieval-Augmented Generation for LLM-Based Recommendation](https://arxiv.org/pdf/2511.15141) - Nov 19 [Noise-Robust Abstractive Compression in Retrieval-Augmented Language Models](https://arxiv.org/pdf/2512.08943) - Nov 18 [LiveRAG: A diverse Q&A dataset with varying difficulty level for RAG evaluation](https://arxiv.org/pdf/2511.14531) - Nov 17 [TelcoAI: Advancing 3GPP Technical Specification Search through Agentic Multi-Modal Retrieval-Augmented Generation](https://arxiv.org/pdf/2601.16984) - Nov 16 [TAdaRAG: Task Adaptive Retrieval-Augmented Generation via On-the-Fly Knowledge Graph Construction](https://arxiv.org/pdf/2511.12520) - Nov 15 [MME-RAG: Multi-Manager-Expert Retrieval-Augmented Generation for Fine-Grained Entity Recognition in Task-Oriented Dialogues](https://arxiv.org/pdf/2511.12213) - Nov 13 [Modeling Uncertainty Trends for Timely Retrieval in Dynamic RAG](https://arxiv.org/pdf/2511.09980) - Nov 13 [TruthfulRAG: Resolving Factual-level Conflicts in Retrieval-Augmented Generation with Knowledge Graphs](https://arxiv.org/pdf/2511.10375) - Nov 13 [RAGFort: Dual-Path Defense Against Proprietary Knowledge Base Extraction in Retrieval-Augmented Generation](https://arxiv.org/pdf/2511.10128) - Nov 12 [BarrierBench : Evaluating Large Language Models for Safety Verification in Dynamical Systems](https://arxiv.org/pdf/2511.09363) - Nov 10 [Q-RAG: Long Context Multi-step Retrieval via Value-based Embedder Training](https://arxiv.org/pdf/2511.07328) - Nov 10 [A survey: Information search time optimization based on RAG (Retrieval Augmentation Generation) chatbot](https://arxiv.org/pdf/2601.07838) - Nov 8 [Cross-Document Topic-Aligned Chunking for Retrieval-Augmented Generation](https://arxiv.org/pdf/2601.05265) - Nov 8 [Retrieval-Augmented Generation in Medicine: A Scoping Review of Technical Implementations, Clinical Applications, and Ethical Considerations](https://arxiv.org/pdf/2511.05901) - Nov 7 [TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework](https://arxiv.org/pdf/2511.05385) - Nov 6 [RAGalyst: Automated Human-Aligned Agentic Evaluation for Domain-Specific RAG](https://arxiv.org/pdf/2511.04502) - Nov 5 [RAGBoost: Efficient Retrieval-Augmented Generation with Accuracy-Preserving Context Reuse](https://arxiv.org/pdf/2511.03475) - Nov 1 [Zero-RAG: Towards Retrieval-Augmented Generation with Zero Redundant Knowledge](https://arxiv.org/pdf/2511.00505) ### 🔥2025 October - Oct 29 [DIRC-RAG: Accelerating Edge RAG with Robust High-Density and High-Loading-Bandwidth Digital In-ReRAM Computation](https://arxiv.org/pdf/2510.25278) - Oct 28 [Mitigating Hallucination in Large Language Models (LLMs): An Application-Oriented Survey on RAG, Reasoning, and Agentic Systems](https://arxiv.org/pdf/2510.24476) - Oct 28 [META-RAG: Meta-Analysis-Inspired Evidence-Re-Ranking Method for Retrieval-Augmented Generation in Evidence-Based Medicine](https://arxiv.org/pdf/2510.24003) - Oct 28 [PICOs-RAG: PICO-supported Query Rewriting for Retrieval-Augmented Generation in Evidence-Based Medicine](https://arxiv.org/pdf/2510.23998) - Oct 25 [FAIR-RAG: Faithful Adaptive Iterative Refinement for Retrieval-Augmented Generation](https://arxiv.org/pdf/2510.22344) - Oct 24 [InterpDetect: Interpretable Signals for Detecting Hallucinations in Retrieval-Augmented Generation](https://arxiv.org/pdf/2510.21538) - Oct 24 [SUBQRAG: Sub-Question Driven Dynamic Graph RAG](https://arxiv.org/pdf/2510.07718) - Oct 21 [Is Implicit Knowledge Enough for LLMs? A RAG Approach for Tree-based Structures](https://arxiv.org/pdf/2510.10806) - Oct 21 [Query Decomposition for RAG: Balancing Exploration-Exploitation](https://arxiv.org/pdf/2510.18633) - Oct 17 [RAG vs. GraphRAG: A Systematic Evaluation and Key Insights](https://arxiv.org/pdf/2502.11371) - Oct 17 [Stop-RAG: Value-Based Retrieval Control for Iterative RAG](https://arxiv.org/pdf/2510.14337) - Oct 16 [Multimodal RAG for Unstructured Data:Leveraging Modality-Aware Knowledge Graphs with Hybrid Retrieval](https://arxiv.org/pdf/2510.14592) - Oct 16 [MoM: Mixtures of Scenario-Aware Document Memories for Retrieval-Augmented Generation Systems](https://arxiv.org/pdf/2510.14252) - Oct 15 [ReMindRAG: Low-Cost LLM-Guided Knowledge Graph Traversal for Efficient RAG](https://arxiv.org/pdf/2510.13193) - Oct 15 [RAG Meets Temporal Graphs: Time-Sensitive Modeling and Retrieval for Evolving Knowledge](https://arxiv.org/pdf/2510.13590) - Oct 15 [SeCon-RAG: A Two-Stage Semantic Filtering and Conflict-Free Framework for Trustworthy RAG](https://arxiv.org/pdf/2510.09710) - Oct 14 [PRoH: Dynamic Planning and Reasoning over Knowledge Hypergraphs for Retrieval-Augmented Generation](https://arxiv.org/pdf/2510.12434) - Oct 14 [RAG-Anything: All-in-One RAG Framework](https://arxiv.org/pdf/2510.12323) - Oct 13 [Domain-Specific Data Generation Framework for RAG Adaptation](https://arxiv.org/pdf/2510.11217) - Oct 12 [RECON: Reasoning with Condensation for Efficient Retrieval-Augmented Generation](https://arxiv.org/pdf/2510.10448) - Oct 12 [Multimodal Retrieval-Augmented Generation with Large Language Models for Medical VQA](https://arxiv.org/pdf/2510.13856) - Oct 11 [LinearRAG: Linear Graph Retrieval Augmented Generation on Large-scale Corpora](https://arxiv.org/pdf/2510.10114) - Oct 11 [RAG-IGBench: Innovative Evaluation for RAG-based Interleaved Generation in Open-domain Question Answering](https://arxiv.org/pdf/2512.05119) - Oct 10 [Use of Retrieval-Augmented Large Language Model Agent for Long-Form COVID-19 Fact-Checking](https://arxiv.org/pdf/2512.00007) - Oct 10 [Chain-of-Retrieval Augmented Generation](https://arxiv.org/pdf/2501.14342) - Oct 10 [When Retrieval Succeeds and Fails: Rethinking Retrieval-Augmented Generation for LLMs](https://arxiv.org/pdf/2510.09106) - Oct 9 [QAgent: A modular Search Agent with Interactive Query Understanding](https://arxiv.org/pdf/2510.08383) - Oct 9 [STEPER: Step-wise Knowledge Distillation for Enhancing Reasoning Ability in Multi-Step Retrieval-Augmented Language Models](https://arxiv.org/pdf/2510.07923) - Oct 7 [HiPRAG: Hierarchical Process Rewards for Efficient Agentic Retrieval Augmented Generation](https://arxiv.org/pdf/2510.07794) - Oct 6 [MHA-RAG: Improving Efficiency, Accuracy, and Consistency by Encoding Exemplars as Soft Prompts](https://arxiv.org/pdf/2510.05363) - Oct 4 [Beyond Outcome Reward: Decoupling Search and Answering Improves LLM Agents](https://arxiv.org/pdf/2510.04695) - Oct 4 [Equipping Retrieval-Augmented Large Language Models with Document Structure Awareness](https://arxiv.org/pdf/2510.04293) - Oct 2 [Less LLM, More Documents: Searching for Improved RAG](https://arxiv.org/pdf/2510.02657) - Oct 2 [Learning to Route: A Rule-Driven Agent Framework for Hybrid-Source Retrieval-Augmented Generation](https://arxiv.org/pdf/2510.02388) - Oct 2 [Training Dynamics of Parametric and In-Context Knowledge Utilization in Language Models](https://arxiv.org/pdf/2510.02370) - Oct 2 [AccurateRAG: A Framework for Building Accurate Retrieval-Augmented Question-Answering Applications](https://arxiv.org/pdf/2510.02243) - Oct 1 [A Comparison of Independent and Joint Fine-tuning Strategies for Retrieval-Augmented Generation](https://arxiv.org/pdf/2510.01600) - Oct 1 [Fine-tuning with RAG for Improving LLM Learning of New Skills](https://arxiv.org/pdf/2510.01375) - Oct 1 [GRAD: Generative Retrieval-Aligned Demonstration Sampler for Efficient Few-Shot Reasoning](https://arxiv.org/pdf/2510.01165) - Oct 1 [HalluGuard: Evidence-Grounded Small Reasoning Models to Mitigate Hallucinations in Retrieval-Augmented Generation](https://arxiv.org/pdf/2510.00880) ### 🍭2025 September - Sep 30 [RAGferee: Building Contextual Reward Models for Retrieval-Augmented Generation](https://arxiv.org/pdf/2509.26011) - Sep 27 [From Evidence to Trajectory: Abductive Reasoning Path Synthesis for Training Retrieval-Augmented Generation Agents](https://arxiv.org/pdf/2509.23071) - Sep 26 [Beyond RAG vs. Long-Context: Learning Distraction-Aware Retrieval for Efficient Knowledge Grounding](https://arxiv.org/pdf/2509.21865) - Sep 26 [Can Synthetic Query Rewrites Capture User Intent Better than Humans in Retrieval-Augmented Generation?](https://arxiv.org/pdf/2509.22325) - Sep 25 [Concise and Sufficient Sub-Sentence Citations for Retrieval-Augmented Generation](https://arxiv.org/pdf/2509.20859) - Sep 24 [RAR<sup>2</sup>: Retrieval-Augmented Medical Reasoning via Thought-Driven Retrieval](https://arxiv.org/pdf/2509.22713) - Sep 22 [AttnComp: Attention-Guided Adaptive Context Compression for Retrieval-Augmented Generation](https://arxiv.org/pdf/2509.17486) - Sep 21 [Influence Guided Context Selection for Effective Retrieval-Augmented Generation](https://arxiv.org/pdf/2509.21359) - Sep 20 [SKILL-RAG: Self-Knowledge Induced Learning and Filtering for Retrieval-Augmented Generation](https://arxiv.org/pdf/2509.20377) - Sep 19 [Relevance to Utility: Process-Supervised Rewrite for RAG](https://arxiv.org/pdf/2509.15577) - Sep 17 [Improving Context Fidelity via Native Retrieval-Augmented Reasoning](https://arxiv.org/pdf/2509.13683) - Sep 9 [Rethinking LLM Parametric Knowledge as Post-retrieval Confidence for Dynamic Retrieval and Reranking](https://arxiv.org/pdf/2509.06472) - Sep 8 [HANRAG: Heuristic Accurate Noise-resistant Retrieval-Augmented Generation for Multi-hop Question Answering](https://arxiv.org/pdf/2509.09713) - Sep 8 [Domain-Aware RAG: MoL-Enhanced RL for Efficient Training and Scalable Retrieval](https://arxiv.org/pdf/2509.06650) - Sep 8 [HAVE: Head-Adaptive Gating and ValuE Calibration for Hallucination Mitigation in Large Language Models](https://arxiv.org/pdf/2509.06596) - Sep 5 [Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation](https://arxiv.org/pdf/2509.04820) - Sep 5 [KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering](https://arxiv.org/pdf/2509.04716) - Sep 4 [SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment](https://arxiv.org/pdf/2509.03934) - Sep 4 [MobileRAG: Enhancing Mobile Agent with Retrieval-Augmented Generation](https://arxiv.org/pdf/2509.03891) - Sep 2 [Better by Comparison: Retrieval-Augmented Contrastive Reasoning for Automatic Prompt Optimization](https://arxiv.org/pdf/2509.02093) - Sep 1 [REFRAG: Rethinking RAG based Decoding](https://arxiv.org/pdf/2509.01092) - Sep 1 [Towards Open-World Retrieval-Augmented Generation on Knowledge Graph: A Multi-Agent Collaboration Framework](https://arxiv.org/pdf/2509.01238) ### 🍭2025 August - Aug 29 [Atom-Searcher: Enhancing Agentic Deep Research via Fine-Grained Atomic Thought Reward](https://arxiv.org/pdf/2508.12800) - Aug 27 [Understanding and Leveraging the Expert Specialization of Context Faithfulness in Mixture-of-Experts LLMs](https://arxiv.org/pdf/2508.19594) - Aug 27 [Can Compact Language Models Search Like Agents? Distillation-Guided Policy Optimization for Preserving Agentic RAG Capabilities](https://arxiv.org/pdf/2508.20324) - Aug 27 [LFD: Layer Fused Decoding to Exploit External Knowledge in Retrieval-Augmented Generation](https://arxiv.org/pdf/2508.19614) - Aug 26 [Context-Adaptive Synthesis and Compression for Enhanced Retrieval-Augmented Generation in Complex Domains](https://arxiv.org/pdf/2508.19357) - Aug 25 [Improving End-to-End Training of Retrieval-Augmented Generation Models via Joint Stochastic Approximation](https://arxiv.org/pdf/2508.18168) - Aug 24 [CORE: Lossless Compression for Retrieval-Augmented LLMs via Reinforcement Learning](https://arxiv.org/pdf/2508.19282) - Aug 24 [SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer](https://arxiv.org/pdf/2508.17556) - Aug 24 [SSFO: Self-Supervised Faithfulness Optimization for Retrieval-Augmented Generation](https://arxiv.org/pdf/2508.17225) - Aug 21 [Conflict-Aware Soft Prompting for Retrieval-Augmented Generation](https://arxiv.org/pdf/2508.15253) - Aug 21 [Select to Know: An Internal-External Knowledge Self-SelectionFramework for Domain-Specific Question Answering](https://arxiv.org/pdf/2508.15213) - Aug 18 [LeanRAG: Knowledge-Graph-Based Generation with Semantic Aggregation and Hierarchical Retrieval](https://arxiv.org/pdf/2508.10391) - Aug 15 [Cross-Granularity Hypergraph Retrieval-Augmented Generation for Multi-hop Question Answering](https://arxiv.org/pdf/2508.11247) - Aug 14 [SSRL: Self-Search Reinforcement Learning](https://arxiv.org/pdf/2508.10874) - Aug 14 [ComoRAG: A Cognitive-Inspired Memory-Organized RAG for Stateful Long Narrative Reasoning](https://arxiv.org/pdf/2508.10419) - Aug 13 [Towards Self-cognitive Exploration: Metacognitive Knowledge Graph Retrieval Augmented Generation](https://arxiv.org/pdf/2508.09460v1) - Aug 13 [Transforming Questions and Documents for Semantically Aligned Retrieval-Augmented Generation](https://arxiv.org/pdf/2508.09755) - Aug 12 [READER: Retrieval-Assisted Drafter for Efficient LLM Inference](https://arxiv.org/abs/2508.09072) - Aug 12 [REX-RAG: Reasoning Exploration with Policy Correction in Retrieval-Augmented Generation](https://arxiv.org/pdf/2508.08149) - Aug 11 [LAG: Logic-Augmented Generation from a Cartesian Perspective](https://arxiv.org/pdf/2508.05509) - Aug 11 [Careful Queries, Credible Results: Teaching RAG Models Advanced Web Search Tools with Reinforcement Learning](https://arxiv.org/pdf/2508.07956) - Aug 10 [PrLM: Learning Explicit Reasoning for Personalized RAG via Contrastive Reward Optimization](https://arxiv.org/pdf/2508.07342) - Aug 8 [Guided Decoding and Its Critical Role in Retrieval-Augmented Generation](https://arxiv.org/pdf/2509.06631) - Aug 8 [UR<sup>2</sup>: Unify RAG and Reasoning through Reinforcement Learning](https://arxiv.org/pdf/2508.06165) - Aug 8 [Spectrum Projection Score: Aligning Retrieved Summaries with Reader Models in Retrieval-Augmented Generation](https://arxiv.org/pdf/2508.05909v1) - Aug 7 [BEE-RAG: Balanced Entropy Engineering for Retrieval-Augmented Generation](https://www.arxiv.org/pdf/2508.05100) - Aug 6 [PAIRS: Parametric–Verified Adaptive Information Retrieval and Selection for Efficient RAG](https://arxiv.org/pdf/2508.04057) - Aug 5 [Collaborative Chain-of-Agents for Parametric-Retrieved Knowledge Synergy](https://arxiv.org/pdf/2508.01696) - Aug 1 [MAO-ARAG: Multi-Agent Orchestration for Adaptive Retrieval-Augmented Generation](https://arxiv.org/pdf/2508.01005) ### 🍭2025 July - Jul 29 [FrugalRAG: Learning to retrieve and reason for multi-hop QA](https://arxiv.org/pdf/2507.07634) - Jul 25 [Injecting External Knowledge into the Reasoning Process Enhances Retrieval-Augmented Generation](https://arxiv.org/pdf/2507.19333) - Jul 25 [Distilling a Small Utility-Based Passage Selector to Enhance Retrieval-Augmented Generation](https://arxiv.org/pdf/2507.19102v1) - Jul 25 [Query-Aware Graph Neural Networks for Enhanced Retrieval-Augmented Generation](https://www.arxiv.org/pdf/2508.05647) - Jul 23 [HiRAG: Retrieval-Augmented Generation with Hierarchical Knowledge](https://arxiv.org/pdf/2503.10150) - Jul 15 [RAG-R1 : Incentivize the Search and Reasoning Capabilities of LLMs through Multi-query Parallelism](https://arxiv.org/pdf/2507.02962) ### 🍭2025 June - Jun 20 [PreQRAG -- Classify and Rewrite for Enhanced RAG](https://arxiv.org/pdf/2506.17493) - Jun 15 [Intra-Trajectory Consistency for Reward Modeling](https://www.arxiv.org/pdf/2506.09096) - Jun 5 [Knowledgeable-r1: Policy Optimization for Knowledge Exploration in Retrieval-Augmented Generation](https://arxiv.org/pdf/2506.05154v1) - Jun 4 [R-Search: Empowering LLM Reasoning with Search via Multi-Reward Reinforcement Learning](https://arxiv.org/pdf/2506.04185v1) - Jun 2 [ImpRAG: Retrieval-Augmented Generation with Implicit Queries](https://arxiv.org/pdf/2506.02279) ### 🥇ACL 2025 $main$ Methods & Pipeline & Framework - [GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis](https://arxiv.org/pdf/2505.18710) [\[Code\]](https://github.com/liunian-Jay/GainRAG) - [FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation](https://arxiv.org/pdf/2506.08938) [\[Code\]](https://github.com/XMUDeepLIT/Faithful-RAG) - [RPO: Retrieval Preference Optimization for Robust Retrieval-Augmented Generation](https://arxiv.org/pdf/2501.13726v1) - [RankCoT: Refining Knowledge for Retrieval-Augmented Generation through Ranking Chain-of-Thoughts](https://arxiv.org/pdf/2502.17888) - [Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning](https://arxiv.org/pdf/2410.10360) - [RARE: Retrieval-Augmented Reasoning Enhancement for Large Language Models](https://arxiv.org/pdf/2412.02830) - [Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG](https://arxiv.org/pdf/2505.20871) - [MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation](https://arxiv.org/pdf/2501.00332) - [DualRAG: A Dual-Process Approach to Integrate Reasoning and Retrieval for Multi-Hop Question Answering](https://arxiv.org/pdf/2504.18243) - [DioR: Adaptive Cognitive Detection and Contextual Retrieval Optimization for Dynamic Retrieval-Augmented Generation](https://arxiv.org/pdf/2504.10198) - [Hierarchical Document Refinement for Long-context Retrieval-augmented Generation](https://arxiv.org/pdf/2505.10413) - [KiRAG: Knowledge-Driven Iterative Retriever for Enhancing Retrieval-Augmented Generation](https://arxiv.org/pdf/2502.18397) - [Enhancing Retrieval-Augmented Generation via Evidence Tree Search](https://arxiv.org/pdf/2503.20757) - [Mitigating Lost-in-Retrieval Problems in Retrieval Augmented Multi-Hop Question Answering](https://arxiv.org/pdf/2502.14245) - [SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generation](https://arxiv.org/pdf/2406.19215) - [TC–RAG: Turing–Complete RAG’s Case study on Medical LLM Systems](https://arxiv.org/pdf/2408.09199) - [Removal of Hallucination on Hallucination: Debate-Augmented RAG](https://arxiv.org/pdf/2505.18581) - [Can We Further Elicit Reasoning in LLMs? Critic-Guided Planning with Retrieval-Augmentation for Solving Challenging Tasks](https://arxiv.org/pdf/2410.01428) - [Optimizing Question Semantic Space for Dynamic Retrieval-Augmented Multi-hop Question Answering](https://arxiv.org/pdf/2506.00491) - [UniRAG: Unified Query Understanding Method for Retrieval Augmented Generation](https://openreview.net/attachment?id=h68SaHDtal&name=pdf) - [DRAG: Distilling RAG for SLMs from LLMs to Transfer Knowledge and Mitigate Hallucination via Evidence and Graph-based Distillation](https://arxiv.org/pdf/2506.01954) - [Lexical Diversity-aware Relevance Assessment for Retrieval-Augmented Generation](https://openreview.net/pdf?id=omv3VfVIQt) - [RAG-Critic: Leveraging Automated Critic-Guided Agentic Workflow for Retrieval Augmented Generation](https://aclanthology.org/2025.acl-long.179.pdf) - [Sparse Latents Steer Retrieval-Augmented Generation](https://aclanthology.org/2025.acl-long.228.pdf) - [GRAT: Guiding Retrieval-Augmented Reasoning through Process Rewards Tree Search](https://aclanthology.org/2025.acl-long.1352.pdf) - [LLMs Trust Humans More, That’s a Problem! Unveiling and Mitigating the Authority Bias in Retrieval-Augmented Generation](https://aclanthology.org/2025.acl-long.1400.pdf) - [Shifting from Ranking to Set Selection for Retrieval Augmented Generation](https://aclanthology.org/2025.acl-long.861.pdf) - [Dialogue-RAG: Enhancing Retrieval for LLMs via Node-Linking Utterance Rewriting](https://aclanthology.org/2025.acl-long.1191.pdf) - [SGIC: A Self-Guided Iterative Calibration Framework for RAG](https://aclanthology.org/2025.acl-long.1376.pdf) Benchmark & Evaluation & Analysis - [SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of Large Language Model](https://arxiv.org/pdf/2501.18636) - [HybGRAG: Hybrid Retrieval-Augmented Generation on Textual and Relational Knowledge Bases](https://arxiv.org/pdf/2412.16311) - [RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework](https://arxiv.org/pdf/2408.01262) - [Unanswerability Evaluation for Retrieval Augmented Generation](https://arxiv.org/pdf/2412.12300) - [MEMERAG: A Multilingual End-to-End Meta-Evaluation Benchmark for Retrieval Augmented Generation](https://arxiv.org/pdf/2502.17163) - [Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models](https://arxiv.org/pdf/2410.07176) - [The Distracting Effect: Understanding Irrelevant Passages in RAG](https://arxiv.org/pdf/2505.06914) - [Pandora’s Box or Aladdin’s Lamp: A Comprehensive Analysis Revealing the Role of RAG Noise in Large Language Models](https://arxiv.org/pdf/2408.13533) - [A Reality Check on Context Utilisation for Retrieval-Augmented Generation](https://arxiv.org/pdf/2412.17031) - [MT-RAIG: Novel Benchmark and Evaluation Framework for Retrieval-Augmented Insight Generation over Multiple Tables](https://arxiv.org/pdf/2502.11735) - [On the Robustness of RAG Systems in Educational Question Answering under Knowledge Discrepancies](https://aclanthology.org/2025.acl-short.16.pdf) Chunk & Database - [MoC: Mixtures of Text Chunking Learners for Retrieval-Augmented Generation System](https://arxiv.org/pdf/2503.09600) - [HoH: A Dynamic Benchmark for Evaluating the Impact of Outdated Information on Retrieval-Augmented Generation](https://arxiv.org/pdf/2503.04800) - [Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphs](https://arxiv.org/pdf/2410.11001) Application - [Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications](https://arxiv.org/pdf/2501.02460) - [Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation](https://arxiv.org/pdf/2501.02226) - [Medical Graph RAG: Evidence-based Medical Large Language Model via Graph Retrieval-Augmented Generation](https://arxiv.org/pdf/2408.04187) - [VISA: Retrieval Augmented Generation with Visual Source Attribution](https://arxiv.org/pdf/2412.14457) - [The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit](https://arxiv.org/pdf/2501.02173) - [HyKGE: A Hypothesis Knowledge Graph Enhanced RAG Framework for Accurate and Reliable Medical LLMs Responses](https://arxiv.org/pdf/2312.15883) - [NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answering](https://arxiv.org/abs/2505.19754) - [CoRe-MMRAG: Cross-Source Knowledge Reconciliation for Multimodal RAG](https://arxiv.org/pdf/2506.02544) - [Retrieval-Augmented Fine-Tuning With Preference Optimization For Visual Program Generation](https://arxiv.org/pdf/2502.16529) $fingdings$ - [Bridging Relevance and Reasoning: Rationale Distillation in Retrieval-Augmented Generation](https://arxiv.org/pdf/2412.08519) - [SimGRAG: Leveraging Similar Subgraphs for Knowledge Graphs Driven Retrieval-Augmented Generation](https://arxiv.org/pdf/2412.15272) - [EXIT: Context-Aware Extractive Compression for Enhancing Retrieval-Augmented Generation](https://arxiv.org/pdf/2412.12559) - [Judge as A Judge: Improving the Evaluation of Retrieval-Augmented Generation through the Judge-Consistency of Large Language Models](https://arxiv.org/pdf/2502.18817) - [RASD: Retrieval-Augmented Speculative Decoding](https://arxiv.org/pdf/2503.03434) - [FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs](https://arxiv.org/pdf/2501.09957) - [Towards Adaptive Memory-Based Optimization for Enhanced Retrieval-Augmented Generation](https://arxiv.org/pdf/2504.05312) - [Retrieval-Augmented Process Reward Model for Generalizable Mathematical Reasoning](https://arxiv.org/pdf/2502.14361) - [Fine-grained Knowledge Enhancement for Retrieval-Augmented Generation](https://arxiv.org/pdf/2502.20964) - [GeAR: Graph-enhanced Agent for Retrieval-augmented Generation](https://arxiv.org/pdf/2412.18431) - [CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Control](https://arxiv.org/pdf/2405.18727) - [RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization](https://arxiv.org/pdf/2502.10993) - [The Silent Saboteur: Imperceptible Adversarial Attacks against Black-Box Retrieval-Augmented Generation Systems](https://www.arxiv.org/pdf/2505.18583) - [PISCO: Pretty Simple Compression for Retrieval-Augmented Generation](https://arxiv.org/pdf/2501.16075) - [RAPID: Efficient Retrieval-Augmented Long Text Generation with Writing Planning and Information Discovery](https://arxiv.org/pdf/2503.00751) - [Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation](https://arxiv.org/pdf/2502.08826) - [CausalRAG: Integrating Causal Graphs into Retrieval-Augmented Generation](https://arxiv.org/pdf/2503.19878) - [Mitigating Bias in RAG: Controlling the Embedder](https://arxiv.org/pdf/2502.17390) - [HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval Augmented Generation](https://arxiv.org/pdf/2502.12442) - [SynapticRAG: Enhancing Temporal Memory Retrieval in Large Language Models through Synaptic Mechanisms](https://arxiv.org/pdf/2410.13553v2) - [HASH-RAG: Bridging Deep Hashing with Retriever for Efficient, Fine Retrieval and Augmented Generation](https://arxiv.org/pdf/2505.16133v3) - [RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment](https://arxiv.org/pdf/2412.13746) - [LLMs are Biased Evaluators But Not Biased for Retrieval Augmented Generation](https://arxiv.org/pdf/2410.20833) - [Accelerating Adaptive Retrieval Augmented Generation via Instruction-Driven Representation Reduction of Retrieval Overlaps](https://arxiv.org/pdf/2505.12731) - [Evaluation of Attribution Bias in Generator-Informed Retrieval-Augmented Large Language Models](https://arxiv.org/pdf/2410.12380) - [Axiomatic Analysis of Uncertainty Estimation For Retrieval Augmented Generation](https://openreview.net/pdf?id=kaPcDVLZEm) - [ECoRAG: Evidentiality-guided Compression for Long Context RAG](https://arxiv.org/pdf/2506.05167) - [GNN-RAG: Graph Neural Retrieval for Efficient Large Language Model Reasoning on Knowledge Graphs](https://arxiv.org/pdf/2405.20139) - [LTRAG: Enhancing autoformalization and self-refinement for logical reasoning with Thought-Guided RAG](https://openreview.net/pdf?id=6WQZCc9qQ1) - [Toward Structured Knowledge Reasoning: Contrastive Retrieval-Augmented Generation on Experience](https://arxiv.org/pdf/2506.00842) - [Document Segmentation Matters for Retrieval-Augmented Generation](https://openreview.net/pdf?id=yToEot3imW) - [Exploring Knowledge Filtering for Retrieval-Augmented Discriminative Tasks](https://aclanthology.org/2025.findings-acl.86.pdf) - [TreeRAG: Unleashing the Power of Hierarchical Storage for Enhanced Knowledge Retrieval in Long Documents](https://aclanthology.org/2025.findings-acl.20.pdf) - [EC-RAFT: Automated Generation of Clinical Trial Eligibility Criteria through Retrieval-Augmented Fine-Tuning](https://aclanthology.org/2025.findings-acl.491.pdf) - [RASPberry: Retrieval-Augmented Monte Carlo Tree Self-Play with Reasoning Consistency for Multi-Hop Question Answering](https://aclanthology.org/2025.findings-acl.587.pdf) - [Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection](https://aclanthology.org/2025.findings-acl.1263.pdf) - [All That Glitters is Not Gold: Improving Robust Retrieval-Augmented Language Models with Fact-Centric Preference Alignment](https://aclanthology.org/2025.findings-acl.588.pdf) ### 🍭2025 May - May 30 [ComposeRAG: A Modular and Composable RAG for Corpus-Grounded Multi-Hop Question Answering](https://arxiv.org/pdf/2506.00232) - May 30 [Pangu DeepDiver: Adaptive Search Intensity Scaling via Open-Web Reinforcement Learning](https://arxiv.org/pdf/2505.24332) - May 30 [ClueAnchor: Clue-Anchored Knowledge Reasoning Exploration and Optimization for Retrieval-Augmented Generation](https://arxiv.org/pdf/2505.24388) - May 26 [R3-RAG: Learning Step-by-Step Reasoning and Retrieval for LLMs via Reinforcement Learning](https://arxiv.org/pdf/2505.23794) - May 26 [Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers](https://arxiv.org/pdf/2505.20128) - May 24 [GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis](https://arxiv.org/pdf/2505.18710) [\[Code\]](https://github.com/liunian-Jay/GainRAG) - May 23 [Curriculum Guided Reinforcement Learning for Efficient Multi Hop Retrieval Augmented Generation](http://export.arxiv.org/pdf/2505.17391) - May 22 [C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like Retrieval-Augmented Generation](https://arxiv.org/pdf/2502.06205) - May 22 [R1-Searcher++: Incentivizing the Dynamic Knowledge Acquisition of LLMsvia Reinforcement Learning](https://arxiv.org/pdf/2505.17005) - May 22 [SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis](https://arxiv.org/pdf/2505.16834) - May 22 [O<sup>2</sup>-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering](https://arxiv.org/pdf/2505.16582) - May 22 [Attributing Response to Context: A Jensen–Shannon Divergence Driven Mechanistic Study of Context Attribution in Retrieval-Augmented Generation](https://arxiv.org/pdf/2505.16415) - May 21 [Ranking Free RAG: Replacing Re-ranking with Selection in RAG for Sensitive Domains](https://arxiv.org/pdf/2505.16014) - May 20 [s3: You Don’t Need That Much Data to Train a Search Agent via RL](https://arxiv.org/pdf/2505.14146) - May 19 [Finetune-RAG: Fine-Tuning Language Models to Resist Hallucination in Retrieval-Augmented Generation](https://arxiv.org/pdf/2505.10792) - May 15 [CL-RAG: Bridging the Gap in Retrieval-Augmented Generation with Curriculum Learning](https://arxiv.org/pdf/2505.10493) - May 8 [Rank-R1: Enhancing Reasoning in LLM-based Document Rerankers via Reinforcement Learning](https://arxiv.org/pdf/2503.06034) - May 6 [An Analysis of Hyper-Parameter Optimization Methods for Retrieval Augmented Generation](https://arxiv.org/pdf/2505.03452) - May 5 [Direct Retrieval-augmented Optimization: Synergizing Knowledge Selection and Language Models](https://arxiv.org/pdf/2505.03075) - May 2 [Retrieval Augmented Learning: A Retrial-based Large Language Model Self-Supervised Learning and Autonomous Knowledge Generation](https://arxiv.org/pdf/2505.01073) ### 🍭2025 April - Apri 25 [DualRAG: A Dual-Process Approach to Integrate Reasoning and Retrieval for Multi-Hop Question Answering](https://arxiv.org/pdf/2504.18243) - Apri 23 [Credible plan-driven RAG method for Multi-hop Question Answering](https://arxiv.org/pdf/2504.16787) - Apri 22 [Exploiting Contextual Knowledge in LLMs through V-usable Information based Layer Enhancement](https://arxiv.org/pdf/2504.15630) - Apri 21 [AlignRAG: An Adaptable Framework for Resolving Misalignments in Retrieval-Aware Reasoning of RAG](https://arxiv.org/pdf/2504.14858) - Apri 17 [DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments](https://arxiv.org/pdf/2504.03160) - Apri 17 [CDF-RAG: Causal Dynamic Feedback for Adaptive Retrieval-Augmented Generation](https://arxiv.org/pdf/2504.12560) - Apri 17 [Accommodate Knowledge Conflicts in Retrieval-augmented LLMs: Towards Reliable Response Generation in the Wild](https://arxiv.org/pdf/2504.12982) - Apri 17 [ACoRN: Noise-Robust Abstractive Compression in Retrieval-Augmented Language Models](https://arxiv.org/pdf/2504.12673) - Apri 15 [Preference-based Learning with Retrieval Augmented Generation for Conversational Question Answering](https://arxiv.org/pdf/2503.22303) - Apri 10 [Plan-and-Refine: Diverse and Comprehensive Retrieval-Augmented Generation](https://arxiv.org/pdf/2504.07794) - Apri 8 [Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning](https://arxiv.org/pdf/2503.09516) - Apri 7 [Collab-RAG: Boosting Retrieval-Augmented Generation for Complex Question Answering via White-Box and Black-Box LLM Collaboration](https://arxiv.org/pdf/2504.04915) - Apri 4 [Efficient Dynamic Clustering-Based Document Compression for Retrieval-Augmented-Generation](https://arxiv.org/pdf/2504.03165) - Apri 3 [HyperRAG: Enhancing Quality-Efficiency Tradeoffs in Retrieval-Augmented Generation with Reranker KV-Cache Reuse](https://arxiv.org/pdf/2504.02921) - Apri 3 [Scaling Test-Time Inference with Policy-Optimized, Dynamic Retrieval-Augmented Generation via KV Caching and Decoding](https://arxiv.org/pdf/2504.01281) - Apri 1 [CoRanking: Collaborative Ranking with Small and Large Ranking Agents](https://arxiv.org/pdf/2503.23427) ### 🍭2025 March - Mar 31 [Insight-RAG: Enhancing LLMs with Insight-Driven Augmentation](https://arxiv.org/pdf/2504.00187) - Mar 31 [UltraRAG: A Modular and Automated Toolkit for Adaptive Retrieval-Augmented Generation](https://arxiv.org/pdf/2504.08761) - Mar 31 [Better wit than wealth: Dynamic Parametric Retrieval Augmented Generation for Test-time Knowledge Enhancement](https://arxiv.org/pdf/2503.23895) - Mar 30 [RARE: Retrieval-Augmented Reasoning Modeling](https://arxiv.org/pdf/2503.23513) - Mar 28 [Preference-based Learning with Retrieval Augmented Generation for Conversational Question Answering](https://arxiv.org/pdf/2503.22303) - Mar 27 [ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large Reasoning Models with Iterative Retrieval Augmented Generation](https://arxiv.org/pdf/2503.21729v1) - Mar 23 [ExpertRAG: Efficient RAG with Mixture of Experts -- Optimizing Context Retrieval for Adaptive LLM Responses](https://arxiv.org/pdf/2504.08744) - Mar 20 [Parameters vs. Context: Fine-Grained Control of Knowledge Reliance in Language Models](https://arxiv.org/abs/2503.15888) - Mar 11 [OpenRAG: Optimizing RAG End-to-End via In-Context Retrieval Learning](https://arxiv.org/abs/2503.08398) ### 🍭2025 February - Feb 26 [Judge as A Judge: Improving the Evaluation of Retrieval-Augmented Generation through the Judge-Consistency of Large Language Models](https://arxiv.org/pdf/2502.18817) - Feb 25 [RankCoT: Refining Knowledge for Retrieval-Augmented Generation through Ranking Chain-of-Thoughts](https://arxiv.org/pdf/2502.17888) - Feb 25 [Say Less, Mean More: Leveraging Pragmatics in Retrieval-Augmented Generation](https://arxiv.org/pdf/2502.17839) - Feb 25 [DRAMA: Diverse Augmentation from Large Language Models to Smaller Dense Retrievers](https://arxiv.org/pdf/2502.18460) - Feb 20 [Mitigating Lost-in-Retrieval Problems in Retrieval Augmented Multi-Hop Question Answering](https://arxiv.org/pdf/2502.14245) - Feb 19 [RAG-Gym: Optimizing Reasoning and Search Agents with Process Supervision](https://arxiv.org/pdf/2502.13957) - Feb 19 [Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach](https://arxiv.org/abs/2502.14100) - Feb 19 [Towards Adaptive Memory-Based Optimization for Enhanced Retrieval-Augmented Generation](https://arxiv.org/pdf/2504.05312) - Feb 18 [RAG-Reward: Optimizing RAG with Reward Modeling and RLHF](https://arxiv.org/pdf/2501.13264) - Feb 17 [Revisiting Robust RAG: Do We Still Need Complex Robust Training in the Era of Powerful LLMs?](https://www.arxiv.org/pdf/2502.11400) - Feb 16 [RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization](https://arxiv.org/pdf/2502.10993) - Feb 14 [Post-training an LLM for RAG? Train on Self-Generated Demonstrations](https://arxiv.org/pdf/2502.10596) - Feb 3 [DeepRAG: Thinking to Retrieval Step by Step for Large Language Models](https://arxiv.org/pdf/2502.01142) ### 🍭2025 January - Jan 30 [Can we Retrieve Everything All at Once? ARM: An Alignment-Oriented LLM-based Retrieval Method](https://arxiv.org/pdf/2501.18539) - Jan 30 [RbFT: Robust Fine-tuning for Retrieval-Augmented Generation against Retrieval Defects](https://arxiv.org/pdf/2501.18365) - Jan 27 [Parametric Retrieval Augmented Generation](https://arxiv.org/pdf/2501.15915) - Jan 14 [ReARTeR: Retrieval-Augmented Reasoning with Trustworthy Process Rewarding](https://arxiv.org/pdf/2501.07861) - Jan 9 [SUGAR: Leveraging Contextual Confidence for Smarter Retrieval](https://arxiv.org/pdf/2501.04899) - Jan 7 [Retrieval-Augmented Generation by Evidence Retroactivity in LLMs](https://arxiv.org/pdf/2501.05475) - Jan 2 [Synergistic Multi-Agent Framework with Trajectory Learning for Knowledge-Intensive Tasks](https://arxiv.org/pdf/2407.09893) ## 🥇ICML 2025 - [From RAG to Memory: Non-Parametric Continual Learning for Large Language Models](https://arxiv.org/pdf/2502.14802) - [LaRA: Benchmarking Retrieval-Augmented Generation and Long-Context LLMs -- No Silver Bullet for LC or RAG Routing](https://arxiv.org/pdf/2502.09977) - [RAGGED: Towards Informed Design of Retrieval Augmented Generation Systems](https://arxiv.org/pdf/2403.09040) - [DocKS-RAG: Optimizing Document-Level Relation Extraction through LLM-Enhanced Hybrid Prompt Tuning](https://openreview.net/pdf?id=SVl9tIADWV) - [Hierarchical Planning for Complex Tasks with Knowledge Graph-RAG and Symbolic Verification](https://arxiv.org/pdf/2504.04578) - [On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains](https://arxiv.org/pdf/2409.17275) - [C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like Retrieval-Augmented Generation](https://arxiv.org/pdf/2502.06205) - [Long-Context Inference with Retrieval-Augmented Speculative Decoding](https://arxiv.org/pdf/2502.20330) - [Position: Retrieval-augmented systems are currently dangerous medical communicators](https://arxiv.org/pdf/2502.14898v1) ## 🥇ICLR 2025 - [SePer: Measure Retrieval Utility Through The Lens Of Semantic Perplexity Reduction](https://openreview.net/pdf?id=ixMBnOhFGd) - [Inference Scaling for Long-Context Retrieval Augmented Generation](https://arxiv.org/pdf/2410.04343) - [Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refuse](https://arxiv.org/pdf/2409.11242) - [Sufficient Context: A New Lens on Retrieval Augmented Generation Systems](https://arxiv.org/pdf/2411.06037) - [Enhancing Large Language Models' Situated Faithfulness to External Contexts](https://arxiv.org/pdf/2410.14675) - [RAG-SR: Retrieval-Augmented Generation for Neural Symbolic Regression](https://openreview.net/pdf?id=NdHka08uWn) - [SmartRAG: Jointly Learn RAG-Related Tasks From the Environment Feedback](https://arxiv.org/pdf/2410.18141) - [InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales](https://arxiv.org/pdf/2406.13629) - [RAG-DDR: Optimizing Retrieval-Augmented Generation Using Differentiable Data Rewards](https://arxiv.org/pdf/2410.13509) - [TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language Models](https://arxiv.org/pdf/2405.13401) - [Provence: efficient and robust context pruning for retrieval-augmented generation](https://arxiv.org/pdf/2501.16214) - [Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG](https://arxiv.org/pdf/2410.05983) - [LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory](https://arxiv.org/pdf/2410.10813) - [MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models](https://arxiv.org/pdf/2410.13085v1) - [A Theory for Token-Level Harmonization in Retrieval-Augmented Generation](https://arxiv.org/pdf/2406.00944v2) - [SiReRAG: Indexing Similar and Related Information for Multihop Reasoning](https://arxiv.org/pdf/2412.06206) - [VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents](https://arxiv.org/pdf/2410.10594) - [ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretability](https://arxiv.org/pdf/2410.11414) - [ChatQA 2: Bridging the Gap to Proprietary LLMs in Long Context and RAG Capabilities](https://arxiv.org/pdf/2407.14482) - [Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selection](https://arxiv.org/pdf/2405.16178) - [Retrieval or Reasoning: The Roles of Graphs and Large Language Models in Efficient Knowledge-Graph-Based Retrieval-Augmented Generation](https://arxiv.org/pdf/2410.20724) - [Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation](https://arxiv.org/pdf/2407.10805) - [DRoC: Elevating Large Language Models for Complex Vehicle Routing via Decomposed Retrieval of Constraints](https://openreview.net/pdf?id=s9zoyICZ4k) - [RAPID: Retrieval Augmented Training of Differentially Private Diffusion Models](https://openreview.net/pdf?id=txZVQRc2ab) - [Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval Augmented Generation](https://arxiv.org/pdf/2410.03461) - [Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting](https://arxiv.org/pdf/2407.08223) - [Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems](https://arxiv.org/pdf/2402.17840) - [Chunk-Distilled Language Modeling](https://arxiv.org/pdf/2501.00343) - [Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent](https://arxiv.org/pdf/2411.02937) ## 🥇NIPS 2024 - [RAGraph: A General Retrieval-Augmented Graph Learning Framework](https://arxiv.org/pdf/2410.23855) - [RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation](https://arxiv.org/pdf/2408.08067) - [G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering](https://arxiv.org/abs/2402.07630) - [RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs](https://arxiv.org/pdf/2407.02485) - [ChatQA: Surpassing GPT-4 on Conversational QA and RAG](https://arxiv.org/pdf/2401.10225) - [HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models](https://arxiv.org/pdf/2405.14831) - [BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack](https://arxiv.org/pdf/2406.10149) - [Self-Retrieval: End-to-End Information Retrieval with One Large Language Model](https://arxiv.org/abs/2403.00801) - [UDA: A Benchmark Suite for Retrieval Augmented Generation in Real-World Document Analysis](https://arxiv.org/pdf/2406.15187) - [Ad Auctions for LLMs via Retrieval Augmented Generation](https://arxiv.org/pdf/2406.09459) - [ReFIR: Grounding Large Restoration Models with Retrieval Augmentation](https://arxiv.org/pdf/2410.05601) - [TableRAG: Million-Token Table Understanding with Language Models](https://arxiv.org/pdf/2410.04739) - [xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Token](https://arxiv.org/pdf/2405.13792) - [Scaling Retrieval-Based Language Models with a Trillion-Token Datastore](https://arxiv.org/pdf/2407.12854) - [Fine-grained Analysis of In-context Linear Estimation: Data, Architecture, and Beyond](https://arxiv.org/pdf/2407.10005) - [Molecule Generation with Fragment Retrieval Augmentation](https://arxiv.org/pdf/2411.12078) - [WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia](https://arxiv.org/pdf/2406.13805) - [Beyond Prompts: Dynamic Conversational Benchmarking of Large Language Models](https://arxiv.org/pdf/2409.20222) - [ConflictBank: A Benchmark for Evaluating the Influence of Knowledge Conflicts in LLMs](https://arxiv.org/pdf/2408.12076) - [CRAG - Comprehensive RAG Benchmark](https://arxiv.org/pdf/2406.04744) ### 🍭2024 December - Dec 19 [PA-RAG: RAG Alignment via Multi-Perspective Preference Optimization](https://arxiv.org/pdf/2412.14510) - Dec 11 [DADIO: Bridging Relevance and Reasoning: Rationale Distillation in Retrieval-Augmented Generation](https://arxiv.org/pdf/2412.08519) - Dec 3 [RARE: Retrieval-Augmented Reasoning Enhancement for Large Language Models](https://arxiv.org/pdf/2412.02830) ### 🍭2024 November - Nov 1 [CORAG: A Cost-Constrained Retrieval Optimization System for Retrieval-Augmented Generation](https://arxiv.org/pdf/2411.00744) ### 🍭2024 October - Oct 12 [Toward General Instruction-Following Alignment for Retrieval-Augmented Generation](https://arxiv.org/pdf/2410.09584) - Oct 11 [STRUCTRAG: BOOSTING KNOWLEDGE INTENSIVE REASONING OF LLMS VIA INFERENCE-TIME HYBRID INFORMATION STRUCTURIZATION](https://arxiv.org/pdf/2410.08815) - Oct 11 [Retriever-and-Memory: Towards Adaptive Note-Enhanced Retrieval-Augmented Generation](https://arxiv.org/pdf/2410.08821) - Oct 9 [Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models](https://arxiv.org/pdf/2410.07176v1) - Oct 7 [TableRAG: Million-Token Table Understanding with Language Models](https://arxiv.org/pdf/2410.04739) - Oct 6 [Inference Scaling for Long-Context Retrieval Augmented Generation](https://arxiv.org/pdf/2410.04343) - Oct 4 [How Much Can RAG Help the Reasoning of LLM?](https://arxiv.org/pdf/2410.02338) - Oct 2[OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs](https://arxiv.org/pdf/2409.05152) - Oct 2 [OPEN-RAG: Enhanced Retrieval-Augmented Reasoning with Open-Source Large Language Models](https://arxiv.org/pdf/2410.01782) ### 🍭2024 September - Sep 23 [Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely](https://arxiv.org/pdf/2409.14924) - Sep 4 [Diversify-verify-adapt: Efficient and Robust Retrieval-Augmented Ambiguous Question Answering](https://arxiv.org/pdf/2409.02361) ### 🍭2024 August - Aug 30 [MaFeRw: Query Rewriting with Multi-Aspect Feedbacks for Retrieval-Augmented Large Language Models](https://arxiv.org/pdf/2408.17072v1) - Aug 29 [LRP4RAG: Detecting Hallucinations in Retrieval-Augmented Generation via Layer-wise Relevance Propagation](https://arxiv.org/pdf/2408.15533v2) - Aug 21 [RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation](https://arxiv.org/pdf/2408.11381) - Aug 21 [Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs](https://arxiv.org/abs/2408.12060) - Aug 20 [Analysis of Plan-based Retrieval for Grounded Text Generation](https://arxiv.org/pdf/2408.10490) - Aug 20 [Hierarchical Retrieval-Augmented Generation Model with Rethink for Multi-hop Question Answering](https://arxiv.org/abs/2408.11875) - Aug 19 [KaPO: Knowledge-aware Preference Optimization for Controllable Knowledge Selection in Retrieval-Augmented Language Models](https://arxiv.org/pdf/2408.03297) - Aug 17 [TC-RAG:Turing-Complete RAG's Case study on Medical LLM Systems](https://arxiv.org/abs/2408.09199) - Aug 16 [Meta Knowledge for Retrieval Augmented Large Language Models](https://arxiv.org/abs/2408.09017) - Aug 7 [EfficientRAG: Efficient Retriever for Multi-Hop Question Answering](https://arxiv.org/pdf/2408.04259) - Aug 7 [Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation](https://arxiv.org/pdf/2408.04187) - Aug 7 [Exploring RAG-based Vulnerability Augmentation with LLMs](https://arxiv.org/pdf/2408.04125) - Aug 7 [Wiping out the limitations of Large Language Models -- A Taxonomy for Retrieval Augmented Generation](https://arxiv.org/pdf/2408.02854) - Aug 2 [BioRAG: A RAG-LLM Framework for Biological Question Reasoning](https://arxiv.org/pdf/2408.01107) - Aug 2 [Adaptive Contrastive Decoding in Retrieval-Augmented Generation for Handling Noisy Contexts](https://arxiv.org/pdf/2408.01084) ### 🍭2024 July - Jul 29 [Improving Retrieval Augmented Language Model with Self-Reasoning](https://arxiv.org/pdf/2407.19813) - Jul 29 [Enhancing Code Translation in Language Models with Few-Shot Learning via Retrieval-Augmented Generation](https://arxiv.org/pdf/2407.19619) - Jul 28 [Enhancing Code Translation in Language Models with Few-Shot Learning via Retrieval-Augmented Generation](https://arxiv.org/pdf/2407.19619) - Jul 25 [Modular RAG: Transforming RAG Systems into LEGO-like Reconfigurable Frameworks](https://arxiv.org/pdf/2407.21059) - Jul 20 [Golden-Retriever: High-Fidelity Agentic Retrieval Augmented Generation for Industrial Knowledge Base](https://arxiv.org/pdf/2408.00798) - Jul 19 [RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering](https://arxiv.org/pdf/2407.13998) - Jul 17 [Optimizing Query Generation for Enhanced Document Retrieval in RAG](https://arxiv.org/pdf/2407.12325) - Jul 11 [Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting](https://arxiv.org/pdf/2407.08223?trk=public_post_comment-text) - jul 2 [RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs](https://arxiv.org/pdf/2407.02485) - Jul 1 [Searching for Best Practices in Retrieval-Augmented Generation](https://arxiv.org/pdf/2407.01219?trk=public_post_comment-text) ### 🍭2024 June - Jun 29 [From RAG to RICHES:Retrieval Interlaced with Sequence Generation](https://arxiv.org/pdf/2407.00361) - Jun 27 [SEAKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Genaration](https://arxiv.org/pdf/2406.19215) - Jun 27 [Unified Active Retrieval for Retrieval Augmented Generation](https://arxiv.org/pdf/2406.12534) - Jun 27 [CrAM: Credibility-Aware Attention Modification in LLMs for Combating Misinformation in RAG](https://arxiv.org/pdf/2406.11497) - Jun 25 [Entropy-Based Decoding for Retrieval-Augmented Large Language Models](https://arxiv.org/pdf/2406.17519) - Jun 21 [RichRAG: Crafting Rich Responses for Multi-faceted Queries in Retrieval-Augmented Generation](https://arxiv.org/pdf/2406.12566) - Jun 21 [LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMs](https://arxiv.org/pdf/2406.15319?fbclid=IwZXh0bgNhZW0CMTAAAR0kiZE83xw45pTDrykhxRUoIkFJJecrR09nDIFd_M96h9_RCCqp04mvx44_aem_vvI_bJ5zlcSTvbdAcAUPZA) - Jun 19 [R<sup>2</sup>AG: Incorporating Retrieval Information into Retrieval Augmented Generation](https://arxiv.org/pdf/2406.13249) - Jun 19 [INSTRUCTRAG: Instructing Retrieval-Augmented Generation with Explicit Denoising](https://arxiv.org/pdf/2406.13629) - Jun 18 [Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approach](https://arxiv.org/pdf/2407.13101) - Jun 18 [PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers](https://arxiv.org/pdf/2406.12430) - Jun 18 [Retrieval Meets Reasoning: Dynamic In-Context Editing for Long-Text Understanding](https://arxiv.org/pdf/2406.12331) - Jun 12 [Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation](https://arxiv.org/pdf/2402.18150) - Jun 7 [Multi-Head RAG: Solving Multi-Aspect Problems with LLMs](https://arxiv.org/pdf/2406.05085?trk=public_post_comment-text) - Jun 7 [CRAG - Comprehensive RAG Benchmark](https://arxiv.org/pdf/2406.04744) - Jun 1 [Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented Generation](https://arxiv.org/pdf/2406.00456) ### 🍭2024 May - May 30 [GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning](https://arxiv.org/pdf/2405.20139) - May 26 [Cocktail: A Comprehensive Information Retrieval Benchmark with LLM-Generated Documents Integration](https://arxiv.org/pdf/2405.16546) - May 23 [HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models](https://arxiv.org/pdf/2405.14831) - May 22 [xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Token](https://arxiv.org/pdf/2405.13792) - May 14 [ERATTA: Extreme RAG for Table To Answers with Large Language Models](https://arxiv.org/pdf/2405.03963) - May 13 [Evaluation of Retrieval-Augmented Generation: A Survey](https://arxiv.org/pdf/2405.07437) - May 12 [DUETRAG: COLLABORATIVE RETRIEVAL-AUGMENTEDGENERATION](https://arxiv.org/pdf/2405.13002) - May 6 [ERAGent: Enhancing Retrieval-Augmented Language Models with Improved Accuracy, Efficiency, and Personalization](https://arxiv.org/pdf/2405.06683) ### 🍭2024 April - Apr 26 [Better Synthetic Data by Retrieving and Transforming Existing Datasets](https://arxiv.org/pdf/2404.14361) - Apr 22 [LLMs Know What They Need: Leveraging a Missing Information Guided Framework to Empower Retrieval-Augmented Generation](https://arxiv.org/pdf/2404.14043) - Apr 16 [How faithful are RAG models? Quantifying the tug-of-war between RAG and LLMs' internal prior](https://arxiv.org/pdf/2404.10198?trk=public_post_comment-text) - Apr 12 [Reducing hallucination in structured outputs via Retrieval-Augmented Generation](https://arxiv.org/pdf/2404.08189) - Apr 1 [ARAGOG: Advanced RAG Output Grading](https://arxiv.org/pdf/2404.01037.pdf?trk=public_post_comment-text) ### 🍭2024 March - Mar 21 [FIT-RAG: Black-Box RAG with Factual Information and Token Reduction](https://arxiv.org/pdf/2403.14374) - Mar 15 [RAFT: Adapting Language Model to Domain Specific RAG](https://arxiv.org/pdf/2403.10131?trk=public_post_comment-text) - Mar 14 [G-Retriever: Retrieval-AugmeAprnted Generation for Textual Graph Understanding and Question Answering](https://arxiv.org/pdf/2402.07630) - Mar 8 [RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation](https://arxiv.org/pdf/2403.05313v1?trk=public_post_comment-text) ### 🍭2024 February - Feb 27 [REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering](https://arxiv.org/pdf/2402.17497) - Feb 22 [Tug-of-War Between Knowledge: Exploring and Resolving Knowledge Conflicts in Retrieval-Augmented Language Models](https://arxiv.org/pdf/2402.14409) - Feb 21 [ACTIVERAG: Revealing the Treasures of Knowledge via Active Learning](https://arxiv.org/pdf/2402.13547) - Feb 16 [Retrieve Only When It Needs: Adaptive Retrieval Augmentation for Hallucination Mitigation in Large Language Models](https://arxiv.org/pdf/2402.10612) - Feb 16 [Corrective Retrieval Augmented Generation](https://arxiv.org/pdf/2401.15884) ### 🍭2024 January - Jan 27 [Enhancing Large Language Model Performance To Answer Questions and Extract Information More Accurately](https://arxiv.org/pdf/2402.01722) - Jan 24 [UniMS-RAG: A Unified Multi-source Retrieval-Augmented Generation for Personalized Dialogue Systems](https://arxiv.org/pdf/2401.13256) ### 🥇EMNLP 2024 $main$ - [BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filtering](https://aclanthology.org/2024.emnlp-main.58.pdf) - [“Glue pizza and eat rocks” - Exploiting Vulnerabilities in Retrieval-Augmented Generative Models](https://aclanthology.org/2024.emnlp-main.96.pdf) - [SEER: Self-Aligned Evidence Extraction for Retrieval-Augmented Generation](https://aclanthology.org/2024.emnlp-main.178.pdf) - [Crafting Personalized Agents through Retrieval-Augmented Generation on Editable Memory Graphs](https://aclanthology.org/2024.emnlp-main.281.pdf) - [REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering](https://aclanthology.org/2024.emnlp-main.321.pdf) - [Model Internals-based Answer Attribution for Trustworthy Retrieval-Augmented Generation](https://aclanthology.org/2024.emnlp-main.347.pdf) - [TimeR<sup>4</sup> : Time-aware Retrieval-Augmented Large Language Models for Temporal Knowledge Graph Question Answering](https://aclanthology.org/2024.emnlp-main.394.pdf) - [Synchronous Faithfulness Monitoring for Trustworthy Retrieval-Augmented Generation](https://aclanthology.org/2024.emnlp-main.527.pdf) - [ATM: Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented Generator](https://aclanthology.org/2024.emnlp-main.610.pdf) - [Lifelong Knowledge Editing for LLMs with Retrieval-Augmented Continuous Prompt Learning](https://aclanthology.org/2024.emnlp-main.751.pdf) - [Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models](https://aclanthology.org/2024.emnlp-main.813.pdf) - [Searching for Best Practices in Retrieval-Augmented Generation](https://aclanthology.org/2024.emnlp-main.981.pdf) - [Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs](https://aclanthology.org/2024.emnlp-main.993.pdf) - [RE-RAG: Improving Open-Domain QA Performance and Interpretability with Relevance Estimator in Retrieval-Augmented Generation](https://aclanthology.org/2024.emnlp-main.1236.pdf) - [LongRAG: A Dual-perspective Retrieval-Augmented Generation Paradigm for Long-Context Question Answering](https://aclanthology.org/2024.emnlp-main.1259.pdf) - [ZEBRA: Zero-Shot Example-Based Retrieval Augmentation for Commonsense Question Answering](https://aclanthology.org/2024.emnlp-main.1251.pdf) - [RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models](https://aclanthology.org/2024.emnlp-main.62.pdf) - [From RAG to Riches: Retrieval Interlaced with Sequence Generation](https://aclanthology.org/2024.emnlp-main.502.pdf) - [Summary of a Haystack: A Challenge to Long-Context LLMs and RAG Systems](https://aclanthology.org/2024.emnlp-main.552.pdf) - [Deciphering the Interplay of Parametric and Non-Parametric Memory in RAG Models](https://aclanthology.org/2024.emnlp-main.943.pdf) - [DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAG](https://aclanthology.org/2024.emnlp-main.762.pdf) - [RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering](https://aclanthology.org/2024.emnlp-main.249.pdf) - [Do You Know What You Are Talking About? Characterizing Query-Knowledge Relevance For Reliable Retrieval Augmented Generation](https://aclanthology.org/2024.emnlp-main.353.pdf) $fingdings$ - [RaFe: Ranking Feedback Improves Query Rewriting for RAG](https://aclanthology.org/2024.findings-emnlp.49.pdf) - [Adaptive Contrastive Decoding in Retrieval-Augmented Generation for Handling Noisy Contexts](https://aclanthology.org/2024.findings-emnlp.136.pdf) - [BSharedRAG: Backbone Shared Retrieval-Augmented Generation for the E-commerce Domain](https://aclanthology.org/2024.findings-emnlp.62.pdf) - [LONG<sup>2</sup>RAG: Evaluating Long-Context & Long-Form Retrieval-Augmented Generation with Key Point Recall](https://aclanthology.org/2024.findings-emnlp.279.pdf) - [Open-RAG: Enhanced Retrieval Augmented Reasoning with Open-Source Large Language Models](https://aclanthology.org/2024.findings-emnlp.831.pdf) - [TRACE the Evidence: Constructing Knowledge-Grounded Reasoning Chains for Retrieval-Augmented Generation](https://aclanthology.org/2024.findings-emnlp.496.pdf) - [BERGEN: A Benchmarking Library for Retrieval-Augmented Generation](https://aclanthology.org/2024.findings-emnlp.449.pdf) - [Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs](https://aclanthology.org/2024.findings-emnlp.459.pdf) - [Retrieval-Augmented Code Generation for Situated Action Generation: A Case Study on Minecraft](https://aclanthology.org/2024.findings-emnlp.652.pdf) - [“Knowing When You Don’t Know”: A Multilingual Relevance Assessment Dataset for Robust Retrieval-Augmented Generation](https://aclanthology.org/2024.findings-emnlp.730.pdf) - [LLMs as Collaborator: Demands-Guided Collaborative Retrieval-Augmented Generation for Commonsense Knowledge-Grounded Open-Domain Dialogue Systems](https://aclanthology.org/2024.findings-emnlp.794.pdf) - [Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation](https://aclanthology.org/2024.findings-emnlp.607.pdf) - [R<sup>2</sup>AG: Incorporating Retrieval Information into Retrieval Augmented Generation](https://aclanthology.org/2024.findings-emnlp.678.pdf) - [RAG-Studio: Towards In-Domain Adaptation Of Retrieval Augmented Generation Through Self-Alignment](https://aclanthology.org/2024.findings-emnlp.41.pdf) - [Unified Active Retrieval for Retrieval Augmented Generation](https://aclanthology.org/2024.findings-emnlp.999.pdf) - [SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation](https://aclanthology.org/2024.findings-emnlp.71.pdf) - [Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework](https://aclanthology.org/2024.findings-emnlp.133.pdf) - [AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation](https://aclanthology.org/2024.findings-emnlp.223.pdf) - [Typos that Broke the RAG’s Back: Genetic Attack on RAG Pipeline by Simulating Documents in the Wild via Low-level Perturbations](https://aclanthology.org/2024.findings-emnlp.161.pdf) ### 🥇ACL 2024 $main$ - [Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation](https://arxiv.org/pdf/2402.18150) - [An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented Generation](https://arxiv.org/pdf/2406.01549) - [Bridging the Preference Gap between Retrievers and LLMs](https://arxiv.org/pdf/2401.06954) - [ARL2: Aligning Retrievers with Black-box Large Language Models via Self-guided Adaptive Relevance Labeling](https://arxiv.org/pdf/2402.13542) - [M-RAG: Reinforcing Large Language Model Performance through Retrieval-Augmented Generation with Multiple Partitions](https://arxiv.org/pdf/2405.16420) - [Generate-then-Ground in Retrieval-Augmented Generation for Multi-hop Question Answering](https://arxiv.org/pdf/2406.14891) - [Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training](https://arxiv.org/pdf/2405.20978) - [RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models](https://arxiv.org/pdf/2401.00396) - [Grounding Language Model with Chunking-Free In-Context Retrieval](https://arxiv.org/pdf/2402.09760) - [On the Role of Long-tail Knowledge in Retrieval Augmented Large Language Models](https://arxiv.org/pdf/2406.16367) - [Landmark Embedding: A Chunking-Free Embedding Method For Retrieval Augmented Long-Context Large Language Models](https://arxiv.org/pdf/2402.11573) - [A Multi-Task Embedder For Retrieval Augmented LLM](https://aclanthology.org/2024.acl-long.194.pdf) - [To Generate or to Retrieve? On the Effectiveness of Artificial Contexts for Medical Open-Domain Question Answering](https://arxiv.org/pdf/2403.01924) - [Blinded by Generated Contexts: How Language Models Merge Generated and Retrieved Contexts When Knowledge Conflicts?](https://arxiv.org/pdf/2401.11911) - [Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs](https://arxiv.org/pdf/2402.12052) - [RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records](https://arxiv.org/pdf/2403.00815) - [DRAGIN: Dynamic Retrieval Augmented Generation based on the Real-time Information Needs of Large Language Models](https://arxiv.org/pdf/2403.10081) - [Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments](https://arxiv.org/pdf/2406.09815) - [Dataflow-Guided Retrieval Augmentation for Repository-Level Code Completion](https://arxiv.org/pdf/2405.19782) - [Understanding Retrieval Robustness for Retrieval-augmented Image Captioning](https://arxiv.org/pdf/2406.02265) - [Spiral of Silence: How is Large Language Model Killing Information Retrieval?—A Case Study on Open Domain Question Answering](https://arxiv.org/pdf/2404.10496) - [REANO: Optimising Retrieval-Augmented Reader Models through Knowledge Graph Generation](https://aclanthology.org/2024.acl-long.115.pdf) - [Synergistic Interplay between Search and Large Language Models for Information Retrieval](https://arxiv.org/pdf/2305.07402) $findings$ - [MORE: Multi-mOdal REtrieval Augmented Generative Commonsense Reasoning](https://aclanthology.org/2024.findings-acl.69.pdf) - [RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback](https://aclanthology.org/2024.findings-acl.281.pdf) - [Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts](https://aclanthology.org/2024.findings-acl.458.pdf) - [When Do LLMs Need Retrieval Augmentation? Mitigating LLMs’ Overconfidence Helps Retrieval Augmentation](https://aclanthology.org/2024.findings-acl.675.pdf) - [RetrievalQA: Assessing Adaptive Retrieval-Augmented Generation for Short-form Open-Domain Question Answering](https://aclanthology.org/2024.findings-acl.415.pdf) - [Retrieval-Augmented Retrieval: Large Language Models are Strong Zero-Shot Retriever](https://aclanthology.org/2024.findings-acl.943.pdf) - [Benchmarking Retrieval-Augmented Generation for Medicine](https://aclanthology.org/2024.findings-acl.372.pdf) - [Unraveling and Mitigating Retriever Inconsistencies in Retrieval-Augmented Large Language Models](https://aclanthology.org/2024.findings-acl.288.pdf) - [ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models](https://aclanthology.org/2024.findings-acl.122.pdf) - [The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)](https://aclanthology.org/2024.findings-acl.267.pdf) ### 🥇ICML 2024 - [C-RAG: Certified Generation Risks for Retrieval-Augmented Language Models](https://arxiv.org/pdf/2402.03181) - [DFA-RAG: Conversational Semantic Router for Large Language Model with Definite Finite Automaton](https://openreview.net/pdf?id=LpAzlcGzJ6) - [InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining](https://arxiv.org/pdf/2310.07713) - [A Statistical Framework for Data-dependent Retrieval-Augmented Models](https://arxiv.org/pdf/2408.15399) - [Superposition Prompting: Improving and Accelerating Retrieval-Augmented Generation](https://arxiv.org/pdf/2404.06910) - [Trustworthy Alignment of Retrieval-Augmented Large Language Models via Reinforcement Learning](https://openreview.net/pdf?id=XwnABAdH5y) - [Understanding Retrieval-Augmented Task Adaptation for Vision-Language Models](https://arxiv.org/pdf/2405.01468) - [Bottleneck-Minimal Indexing for Generative Document Retrieval](https://arxiv.org/pdf/2405.10974) - [PinNet: Pinpoint Instructive Information for Retrieval Augmented Code-to-Text Generation](https://openreview.net/pdf?id=TqcZfMZjgM) - [Retrieval-Augmented Score Distillation for Text-to-3D Generation](https://arxiv.org/pdf/2402.02972) - [Automated Evaluation of Retrieval-Augmented Language Models with Task-Specific Exam Generation](https://arxiv.org/pdf/2405.13622) - [Accelerating Iterative Retrieval-augmented Language Model Serving with Speculation](https://openreview.net/pdf?id=CDnv4vg02f) ### 🥇ICLR 2024 - [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://arxiv.org/pdf/2310.11511) - [BTR: Binary Token Representations for Efficient Retrieval Augmented Language Models](https://arxiv.org/pdf/2310.01329) - [Making Retrieval-Augmented Language Models Robust to Irrelevant Context](https://arxiv.org/pdf/2310.01558) - [RA-DIT: Retrieval-Augmented Dual Instruction Tuning](https://arxiv.org/pdf/2310.01352?trk=public_post_comment-text) - [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](https://arxiv.org/pdf/2401.18059.pdf?utm_referrer=https%3A%2F%2Fdzen.ru%2Fmedia%2Fid%2F5e048b1b2b616900b081f1d9%2F66110fe915ffb223365956df) - [RECOMP: Improving Retrieval-Augmented LMs with Context Compression and Selective Augmentation](https://openreview.net/pdf?id=mlJLVigNHp) - [Retrieval meets Long Context Large Language Models](https://arxiv.org/pdf/2310.03025) - [SuRe: Summarizing Retrievals using Answer Candidates for Open-domain QA of LLMs](https://arxiv.org/pdf/2404.13081) ## Star History [](https://www.star-history.com/#liunian-Jay/Awesome-RAG&Date) Welcome to communicate with us by email at jiangyijcx@163.com
gitextract_blcxf717/ └── README.md
Condensed preview — 1 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (99K chars).
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"preview": "<div align=\"center\">\n <!-- <p align=\"center\"> -->\n <h1 align=\"center\"><strong>Awesome-RAG</strong></h1>\n</div>\n\n<div a"
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]
About this extraction
This page contains the full source code of the liunian-Jay/Awesome-RAG GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 1 files (95.6 KB), approximately 26.7k tokens. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.