幻觉与事实：大型语言模型中事实核查与事实性评估的综述
Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Models
AI总结：
本文综述了大型语言模型中事实核查与事实性评估的关键挑战及方法，强调了提升事实准确性的重要性。
摘要：
大型语言模型（LLMs）是在庞大的、多样化的互联网语料库上进行训练的，这些语料库往往包含不准确或误导性内容。因此，LLMs可能会生成虚假信息，这使得稳健的事实核查至关重要。本文系统地分析了如何评估LLM生成内容的事实准确性，通过探讨关键挑战，如幻觉、数据集限制以及评估指标的可靠性。本文强调了需要强大的事实核查框架，这些框架整合了先进的提示策略、领域特定的微调以及检索增强生成（RAG）方法。本文提出了五个研究问题，以指导对2020至2025年之间近期文献的分析，重点在于评估方法和缓解技术。此外，本文还回顾了指令微调、多代理推理以及用于外部知识访问的RAG框架。关键发现展示了当前指标的局限性，验证的外部证据的重要性，以及通过领域特定定制提高事实一致性。本文强调了构建更加准确、易于理解且具有上下文意识的事实核查的重要性。这些见解有助于推动研究向更加可信的模型发展。
英文摘要：
Large Language Models (LLMs) are trained on vast and diverse internet corpora that often include inaccurate or misleading content. Consequently, LLMs can generate misinformation, making robust fact-checking essential. This review systematically analyzes how LLM-generated content is evaluated for factual accuracy by exploring key challenges such as hallucinations, dataset limitations, and the reliability of evaluation metrics. The review emphasizes the need for strong fact-checking frameworks that integrate advanced prompting strategies, domain-specific fine-tuning, and retrieval-augmented generation (RAG) methods. It proposes five research questions that guide the analysis of the recent literature from 2020 to 2025, focusing on evaluation methods and mitigation techniques. Instruction tuning, multi-agent reasoning, and RAG frameworks for external knowledge access are also reviewed. The key findings demonstrate the limitations of current metrics, the importance of validated external evidence, and the improvement of factual consistency through domain-specific customization. The review underscores the importance of building more accurate, understandable, and context-aware fact-checking. These insights contribute to the advancement of research toward more trustworthy models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
