mlsanityData to 7 Oct 2026

Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation

arXiv 2407.01796 ↗First posted 1 Jul 2024cs.CLcited by 21

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

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) has been widely adopted to enhance Large Language Models (LLMs) in knowledge-intensive tasks. To enhance credibility and verifiability in RAG systems, Attributed Text Generation (ATG) is proposed, which provides citations to retrieval knowledge in LLM-generated responses. Prior methods mainly adopt coarse-grained attributions, with passage-level or paragraph-level references or citations, which fall short in verifiability. This paper proposes ReClaim (Refer & Claim), a fine-grained ATG method that alternates the generation of references and answers step by step. Different from previous coarse-grained attribution, ReClaim provides sentence-level citations in long-form question-answering tasks. With extensive experiments, we verify the effectiveness of ReClaim in extensive settings, achieving a citation accuracy rate of 90%.

Key facts. Cited by 21 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 26 of its 36 references that are in the map. First posted on arXiv 1 Jul 2024.

Builds on

26 of 36 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection17 Oct

Other references · 25

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  5. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  6. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  7. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  8. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
  9. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
  10. 2024LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Modelscited by 1,442
  11. 2022LaMDA: Language Models for Dialog Applicationscited by 1,218
  12. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
13 more
  1. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,050
  2. 2023Enabling Large Language Models to Generate Text with Citationscited by 584
  3. 2019ELI5: Long Form Question Answeringcited by 567
  4. 2021MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontierscited by 466
  5. 2022RARR: Researching and Revising What Language Models Say, Using Language Modelscited by 286
  6. 2022ASQA: Factoid Questions Meet Long-Form Answerscited by 231
  7. 2023WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferencescited by 100
  8. 2023ExpertQA: Expert-Curated Questions and Attributed Answerscited by 96
  9. 2023Llama2Vec: Unsupervised Adaptation of Large Language Models for Dense Retrievalcited by 77
  10. 2021The Web Is Your Oyster - Knowledge-Intensive NLP against a Very Large Web Corpuscited by 60
  11. 2023LLatrieval: LLM-Verified Retrieval for Verifiable Generationcited by 52
  12. 2024Training Language Models to Generate Text with Citations via Fine-grained Rewardscited by 40
  13. 2024Learning to Plan and Generate Text with Citationscited by 28

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 9; 2026: 9.

21 papers.

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  1. 2024Trustworthiness in Retrieval-Augmented Generation Systems: A Surveycited by 83
  2. 2024Speculative RAG: Enhancing Retrieval Augmented Generation through Draftingcited by 76
  3. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generationcited by 34
  4. 2024Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refusecited by 33
  5. 2025EnronQA: Towards Personalized RAG over Private Documentscited by 8
  6. 2025Lessons from Training Grounded LLMs with Verifiable Rewardscited by 8
  7. 2025Attribution, Citation, and Quotation: A Survey of Evidence-based Text Generation with Large Language Modelscited by 7
  8. 2026GRACE: Reinforcement Learning for Grounded Response and Abstention under Contextual Evidencecited by 6
  9. 2025Chain-of-Thought Poisoning Attacks against R1-based Retrieval-Augmented Generation Systemscited by 5
  10. 2025Generation-Time vs. Post-hoc Citation: A Holistic Evaluation of LLM Attributioncited by 5
  11. 2025Large Language Model Sourcing: A Surveycited by 5
  12. 2025Geometric Metrics and LLMs: What They Measure and When They Workcited by 4
9 more
  1. 2026AdversarialCoT: Single-Document Retrieval Poisoning for LLM Reasoningcited by 3
  2. 2026Autonomous Knowledge Graph Exploration with Adaptive Breadth-Depth Retrievalcited by 2
  3. 2026Attributable by Construction: Claim-Anchored Provenance for Multi-Document Summarizationcited by 2
  4. 2026Learning from AVA: Early Lessons from a Curated and Trustworthy Generative AI for Policy and Development Researchcited by 1
  5. 2025Jointly Generating and Attributing Answers using Logits of Document-Identifier Tokenscited by 0
  6. 2026Predict-then-Diffuse: Adaptive Response Length for Compute-Budgeted Inference in Diffusion LLMscited by 0
  7. 2026Explicit Evidence Grounding via Structured Inline Citation Generationcited by 0
  8. 2026Local-to-Global Sentence-Level Graph Reranking for Scientific Synthesiscited by 0
  9. 2026EvidenT: Building Trustworthy Enterprise Assistants through Evidence Groundedness and Traceabilitycited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Concise and Sufficient Sub-Sentence Citations for Retrieval-Augmented Generation
  2. 2025VeriCite: Towards Reliable Citations in Retrieval-Augmented Generation via Rigorous Verification
  3. 2026How Do LLMs Cite? A Mechanistic Interpretation of Attribution in Retrieval-Augmented Generation
  4. 2024On the Capacity of Citation Generation by Large Language Models
  5. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey
  6. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  7. 2025CiteFix: Enhancing RAG Accuracy Through Post-Processing Citation Correction
  8. 2024LLM-Ref: Enhancing Reference Handling in Technical Writing with Large Language Models