mlsanityData to 7 Oct 2026

BIDER: Bridging Knowledge Inconsistency for Efficient Retrieval-Augmented LLMs via Key Supporting Evidence

arXiv 2402.12174 ↗First posted 19 Feb 2024cs.CLcited by 28

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

Abstract (arXiv)

Retrieval-augmented large language models (LLMs) have demonstrated efficacy in knowledge-intensive tasks such as open-domain QA, addressing inherent challenges in knowledge update and factual inadequacy. However, inconsistencies between retrieval knowledge and the necessary knowledge for LLMs, leading to a decline in LLM's answer quality. This paper introduces BIDER, an approach that refines retrieval documents into Key Supporting Evidence (KSE) through knowledge synthesis, supervised fine-tuning (SFT), and preference alignment. We train BIDER by learning from crafting KSE, while maximizing its output to align with LLM's information acquisition preferences through reinforcement learning. Evaluations across five datasets show BIDER boosts LLMs' answer quality by 7% while reducing input content length in retrieval documents by 80%, outperforming existing methods. The proposed KSE simulation effectively equips LLMs with essential information for accurate question answering.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 40 of its 51 references that are in the map. First posted on arXiv 19 Feb 2024.

Builds on

40 of 51 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023PRCA: Fitting Black-Box Large Language Models for Retrieval Question Answering via Pluggable Reward-Driven Contextual Adapter23 Oct

Other references · 39

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2017Proximal Policy Optimization Algorithmscited by 14,441
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  5. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  6. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  7. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  8. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  9. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  10. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  11. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  12. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
27 more
  1. 2020Learning to summarize from human feedbackcited by 2,562
  2. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  3. 2019Fine-Tuning Language Models from Human Preferencescited by 2,124
  4. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 1,916
  5. 2023LongBench: A Bilingual, Multitask Benchmark for Long Context Understandingcited by 1,378
  6. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,362
  7. 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,281
  8. 2021Improving language models by retrieving from trillions of tokenscited by 1,278
  9. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,216
  10. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,054
  11. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 940
  12. 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 888
  13. 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 820
  14. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 786
  15. 2020KILT: a Benchmark for Knowledge Intensive Language Taskscited by 571
  16. 2018Wizard of Wikipedia: Knowledge-Powered Conversational agentscited by 530
  17. 2023LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compressioncited by 443
  18. 2023RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentationcited by 253
  19. 2016Incorporating Copying Mechanism in Sequence-to-Sequence Learningcited by 217
  20. 2020How Context Affects Language Models' Factual Predictionscited by 181
  21. 2023Walking Down the Memory Maze: Beyond Context Limit through Interactive Readingcited by 129
  22. 2019Fine-tune BERT for Extractive Summarizationcited by 122
  23. 2022SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrievalcited by 90
  24. 2023Unlocking Context Constraints of LLMs: Enhancing Context Efficiency of LLMs with Self-Information-Based Content Filteringcited by 62
  25. 2019ChainerRL: A Deep Reinforcement Learning Librarycited by 51
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  27. 2024INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuningcited by 19

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 13; 2025: 11; 2026: 4.

28 papers.

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  1. 2025WebThinker: Empowering Large Reasoning Models with Deep Research Capabilitycited by 320
  2. 2024FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Researchcited by 165
  3. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  4. 2024Trustworthiness in Retrieval-Augmented Generation Systems: A Surveycited by 83
  5. 2024HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systemscited by 37
  6. 2024Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generationcited by 27
  7. 2024RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generationcited by 20
  8. 2025Hierarchical Document Refinement for Long-context Retrieval-augmented Generationcited by 16
  9. 2025CodeRAG: Finding Relevant and Necessary Knowledge for Retrieval-Augmented Repository-Level Code Completioncited by 12
  10. 2024SEER: Self-Aligned Evidence Extraction for Retrieval-Augmented Generationcited by 9
  11. 2025FinSight: Towards Real-World Financial Deep Researchcited by 9
  12. 2024CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation Generationcited by 8
16 more
  1. 2025Bring Your Own Knowledge: A Survey of Methods for LLM Knowledge Expansioncited by 8
  2. 2025HiRA: A Hierarchical Reasoning Framework for Decoupled Planning and Execution in Deep Searchcited by 8
  3. 2025Beyond Pipelines: A Survey of the Paradigm Shift toward Model-Native Agentic AIcited by 8
  4. 2026LaSER: Internalizing Explicit Reasoning into Latent Space for Dense Retrievalcited by 8
  5. 2024DSLR: Document Refinement with Sentence-Level Re-ranking and Reconstruction to Enhance Retrieval-Augmented Generationcited by 5
  6. 2024AssistRAG: Boosting the Potential of Large Language Models with an Intelligent Information Assistantcited by 5
  7. 2024One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Modelscited by 4
  8. 2024Supportiveness-based Knowledge Rewriting for Retrieval-augmented Language Modelingcited by 4
  9. 2026Supervised Fine-Tuning versus Reinforcement Learning: A Study of Post-Training Methods for Large Language Modelscited by 3
  10. 2024SRSA: A Cost-Efficient Strategy-Router Search Agent for Real-world Human-Machine Interactionscited by 2
  11. 2025Less Is More: Elevating RAG via Performance-Driven Context Compressioncited by 2
  12. 2025Oreo: A Plug-in Context Reconstructor to Enhance Retrieval-Augmented Generationcited by 1
  13. 2025Single LLM, Multiple Roles: A Unified Retrieval-Augmented Generation Framework Using Role-Specific Token Optimizationcited by 1
  14. 2026Towards Automatic Soccer Commentary Generation with Knowledge-Enhanced Visual Reasoningcited by 1
  15. 2025On the Diminishing Returns of Complex Robust RAG Training in the Era of Powerful LLMscited by 0
  16. 2026Trajectory-Aware Clinical Risk Prediction via Severity-Grounded Knowledge Graphs and Retrieval-Augmented Generationcited by 0

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  3. 2025Rethinking LLM Parametric Knowledge as Post-retrieval Confidence for Dynamic Retrieval and Reranking
  4. 2025TongSearch-QR: Reinforced Query Reasoning for Retrieval
  5. 2026Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs
  6. 2024Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs
  7. 2025R1-Searcher++: Incentivizing the Dynamic Knowledge Acquisition of LLMs via Reinforcement Learning
  8. 2026OThink-SRR1: Search, Refine and Reasoning with Reinforced Learning for Large Language Models