梳理大模型生成文本的证据链方法，帮用户看清内容来源。
Attribution, Citation, and Quotation: A Survey of Evidence-based Text Generation with Large Language Models
- 归纳134篇论文，建立统一分类体系
- 分析300个评估指标，覆盖7个关键维度
- 适合关注可信AI、可解释生成的研究者
大型语言模型（LLMs）的广泛应用引发了对其可靠性和可信度的担忧。为此，越来越多研究聚焦于基于证据的文本生成，旨在将模型输出与支持性证据关联，以确保可追溯性和可验证性。然而，该领域因术语不一致、评估方式孤立及缺乏统一基准而显得碎片化。为此，我们系统分析了134篇相关论文，提出了一个统一的基于证据的文本生成分类框架，并在七个关键维度上研究了300个评估指标。重点考察了使用引用、归属或引文进行证据生成的方法。在此基础上，分析了该领域的特征与代表性方法，并指出了开放挑战，展望了未来发展方向。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。