mlsanityData to 7 Oct 2026

Learning Fine-Grained Grounded Citations for Attributed Large Language Models

arXiv 2408.04568 ↗First posted 8 Aug 2024cs.CL cs.AIcited by 12

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Abstract (arXiv)

Despite the impressive performance on information-seeking tasks, large language models (LLMs) still struggle with hallucinations. Attributed LLMs, which augment generated text with in-line citations, have shown potential in mitigating hallucinations and improving verifiability. However, current approaches suffer from suboptimal citation quality due to their reliance on in-context learning. Furthermore, the practice of citing only coarse document identifiers makes it challenging for users to perform fine-grained verification. In this work, we introduce FRONT, a training framework designed to teach LLMs to generate Fine-Grained Grounded Citations. By grounding model outputs in fine-grained supporting quotes, these quotes guide the generation of grounded and consistent responses, not only improving citation quality but also facilitating fine-grained verification. Experiments on the ALCE benchmark demonstrate the efficacy of FRONT in generating superior grounded responses and highly supportive citations. With LLaMA-2-7B, the framework significantly outperforms all the baselines, achieving an average of 14.21% improvement in citation quality across all datasets, even surpassing ChatGPT.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 28 of its 45 references that are in the map. First posted on arXiv 8 Aug 2024.

Builds on

28 of 45 references.

References · 28

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16 more
  1. 2021MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontierscited by 466
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 6.

12 papers.

  1. 2024LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-context QAcited by 78
  2. 2024Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refusecited by 33
  3. 2025LongFaith: Enhancing Long-Context Reasoning in LLMs with Faithful Synthetic Datacited by 16
  4. 2025Cite Before You Speak: Enhancing Context-Response Grounding in E-commerce Conversational LLM-Agentscited by 7
  5. 2025REGen: Multimodal Retrieval-Embedded Generation for Long-to-Short Video Editingcited by 1
  6. 2026Beyond Factual Accuracy: Evaluating Global Reasoning Integrity in RAG Systems with LogicScorecited by 1
  7. 2026Learning from AVA: Early Lessons from a Curated and Trustworthy Generative AI for Policy and Development Researchcited by 1
  8. 2026Knowledge Distillation Must Account for What It Losescited by 1
  9. 2026CiteCheck: Retrieval-Grounded Detection of LLM Citation Hallucinations in Scientific Textcited by 1
  10. 2025Citation Failure: Definition, Analysis and Efficient Mitigationcited by 0
  11. 2026PubMed-OCR: PMC Open Access OCR Annotationscited by 0
  12. 2026VetScore: Risk-Weighted Fact Verification for Veterinary Long-Form QA with Citationscited by 0