mlsanityData to 14 Oct 2026

Attribution, Citation, and Quotation: A Survey of Evidence-based Text Generation with Large Language Models

arXiv 2508.15396 ↗First posted 21 Aug 2025cs.CLcited by 7

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Abstract (arXiv)

The increasing adoption of large language models (LLMs) has raised serious concerns about their reliability and trustworthiness. As a result, a growing body of research focuses on evidence-based text generation with LLMs, aiming to link model outputs to supporting evidence to ensure traceability and verifiability. However, the field is fragmented due to inconsistent terminology, isolated evaluation practices, and a lack of unified benchmarks. To bridge this gap, we systematically analyze 134 papers, introduce a unified taxonomy of evidence-based text generation with LLMs, and investigate 300 evaluation metrics across seven key dimensions. Thereby, we focus on approaches that use citations, attribution, or quotations for evidence-based text generation. Building on this, we examine the distinctive characteristics and representative methods in the field. Finally, we highlight open challenges and outline promising directions for future work.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 76 of its 113 references that are in the map. First posted on arXiv 21 Aug 2025.

Builds on

76 of 113 references.

References · 76

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38 more
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50 most cited of 76 All 76 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 5.

7 papers.

  1. 2026From Agent Traces to Trust: A Survey of Evidence Tracing and Execution Provenance in LLM Agentscited by 37
  2. 2026PaperTrail: A Claim-Evidence Interface for Grounding Provenance in LLM-based Scholarly Q&Acited by 2
  3. 2026Are Finer Citations Always Better? Rethinking Granularity for Attributed Generationcited by 2
  4. 2026Attributable by Construction: Claim-Anchored Provenance for Multi-Document Summarizationcited by 2
  5. 2025Human-AI Narrative Synthesis to Foster Shared Understanding in Civic Decision-Makingcited by 1
  6. 2025Proof-Carrying Numbers (PCN): A Protocol for Trustworthy Numeric Answers from LLMs via Claim Verificationcited by 0
  7. 2026Explicit Evidence Grounding via Structured Inline Citation Generationcited by 0