mlsanityData to 7 Oct 2026

Effective Large Language Model Adaptation for Improved Grounding and Citation Generation

arXiv 2311.09533 ↗First posted 16 Nov 2023cs.CLcited by 44

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Abstract (arXiv)

Large language models (LLMs) have achieved remarkable advancements in natural language understanding and generation. However, one major issue towards their widespread deployment in the real world is that they can generate "hallucinated" answers that are not factual. Towards this end, this paper focuses on improving LLMs by grounding their responses in retrieved passages and by providing citations. We propose a new framework, AGREE, Adaptation for GRounding EnhancEment, that improves the grounding from a holistic perspective. Our framework tunes LLMs to selfground the claims in their responses and provide accurate citations to retrieved documents. This tuning on top of the pre-trained LLMs requires well-grounded responses (with citations) for paired queries, for which we introduce a method that can automatically construct such data from unlabeled queries. The selfgrounding capability of tuned LLMs further grants them a test-time adaptation (TTA) capability that can actively retrieve passages to support the claims that have not been grounded, which iteratively improves the responses of LLMs. Across five datasets and two LLMs, our results show that the proposed tuningbased AGREE framework generates superior grounded responses with more accurate citations compared to prompting-based approaches and post-hoc citing-based approaches

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). Builds on 39 of its 47 references that are in the map. First posted on arXiv 16 Nov 2023.

Builds on

39 of 47 references.

References · 39

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27 more
  1. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
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  4. 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 736
  5. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
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  8. 2023Fine-Grained Human Feedback Gives Better Rewards for Language Model Trainingcited by 399
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 16; 2025: 17; 2026: 11.

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  1. 2024VERISCORE: Evaluating the factuality of verifiable claims in long-form text generationcited by 114
  2. 2024ContextCite: Attributing Model Generation to Contextcited by 93
  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. 2025Lifelong Learning of Large Language Model based Agents: A Roadmapcited by 78
  6. 2024CiteME: Can Language Models Accurately Cite Scientific Claims?cited by 45
  7. 2024Training Language Models to Generate Text with Citations via Fine-grained Rewardscited by 40
  8. 2024MedAdapter: Efficient Test-Time Adaptation of Large Language Models towards Medical Reasoningcited by 35
  9. 2024Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refusecited by 33
  10. 2024Source-Aware Training Enables Knowledge Attribution in Language Modelscited by 29
  11. 2024ALR2: A Retrieve-then-Reason Framework for Long-context Question Answeringcited by 25
  12. 2024Towards Fine-Grained Citation Evaluation in Generated Text: A Comparative Analysis of Faithfulness Metricscited by 17
32 more
  1. 2024ALiiCE: Evaluating Positional Fine-grained Citation Generationcited by 15
  2. 2025Attributing Response to Context: A Jensen-Shannon Divergence Driven Mechanistic Study of Context Attribution in Retrieval-Augmented Generationcited by 12
  3. 2025MedCite: Can Language Models Generate Verifiable Text for Medicine?cited by 11
  4. 2025Lessons from Training Grounded LLMs with Verifiable Rewardscited by 8
  5. 2025Generate, Discriminate, Evolve: Enhancing Context Faithfulness via Fine-Grained Sentence-Level Self-Evolutioncited by 7
  6. 2025Cite Before You Speak: Enhancing Context-Response Grounding in E-commerce Conversational LLM-Agentscited by 7
  7. 2025Attribution, Citation, and Quotation: A Survey of Evidence-based Text Generation with Large Language Modelscited by 7
  8. 2024Citekit: A Modular Toolkit for Large Language Model Citation Generationcited by 5
  9. 2025Semantic Mastery: Enhancing LLMs with Advanced Natural Language Understandingcited by 4
  10. 2025Transparentize the Internal and External Knowledge Utilization in LLMs with Trustworthy Citationcited by 4
  11. 2026Beyond Single-shot Writing: Deep Research Agents are Unreliable at Multi-turn Report Revisioncited by 4
  12. 2025Even Small Reasoners Should Quote Their Sources: Introducing the Pleias-RAG Model Familycited by 3
  13. 2026Detecting and Correcting Reference Hallucinations in Commercial LLMs and Deep Research Agentscited by 3
  14. 2024A Comparative Analysis of Faithfulness Metrics and Humans in Citation Evaluationcited by 2
  15. 2026Are Finer Citations Always Better? Rethinking Granularity for Attributed Generationcited by 2
  16. 2024Evaluation of Attribution Bias in Generator-Aware Retrieval-Augmented Large Language Modelscited by 1
  17. 2024Rate, Explain and Cite (REC): Enhanced Explanation and Attribution in Automatic Evaluation by Large Language Modelscited by 1
  18. 2025Inter-Passage Verification for Multi-evidence Multi-answer QAcited by 1
  19. 2025Cite Pretrain: Retrieval-Free Knowledge Attribution for Large Language Modelscited by 1
  20. 2026CAGE: Cognitive Attribution Graphs for Faithful Inline Citation Generation in Long-Form Question Answeringcited by 1
  21. 2025ImageRef-VL: Enabling Contextual Image Referencing in Vision-Language Modelscited by 0
  22. 2025LCDS: A Logic-Controlled Discharge Summary Generation System Supporting Source Attribution and Expert Reviewcited by 0
  23. 2025FinLFQA: Evaluating Attributed Text Generation of LLMs in Financial Long-Form Question Answeringcited by 0
  24. 2026Attribution Techniques for Mitigating Hallucinated Information in RAG Systems: A Surveycited by 0
  25. 2025C2-Cite: Contextual-Aware Citation Generation for Attributed Large Language Modelscited by 0
  26. 2025FineRef: Fine-Grained Error Reflection and Correction for Long-Form Generation with Citationscited by 0
  27. 2026Query Disambiguation via Answer-Free Context: Doubling Performance on Humanity's Last Examcited by 0
  28. 2026Chain of Evidence: Pixel-Level Visual Attribution for Iterative Retrieval-Augmented Generationcited by 0
  29. 2026FaithMed: Training LLMs For Faithful Evidence-Based Medical Reasoningcited by 0
  30. 2026DeepWeaver: Bridging the Evidence Synthesis Gap in Open-Ended Question Answeringcited by 0
  31. 2026Evaluating and Improving Evidence-Grounded Fact-Checking in LLMs via Multi-Round Evidence Ablationcited by 0
  32. 2026DocAttriBench: Benchmarking Answer Grounding in Document Visual Question Answeringcited by 0