UFO：一个统一且灵活的大语言模型事实性评估框架
UFO: a Unified and Flexible Framework for Evaluating Factuality of Large Language Models
AI总结：
本文提出统一灵活的LLM事实性评估框架UFO，系统比较四类事实来源在五类生成任务中的可替代性，发现问答任务依赖人类证据与参考文献，新闻生成任务依赖搜索引擎结果与LLM知识。
摘要：
大语言模型（LLMs）可能生成与人类知识不一致的文本，从而导致事实错误或“幻觉”。现有用于评估LLMs事实性的研究通常先利用一个LLM提取事实声明，再将其与预定义的事实来源进行核验。然而，这些评估指标往往是任务特定的、难以扩展，并且不同任务中事实来源的可替代性尚未得到充分探索。为应对这些挑战，我们将四种可用的事实来源进行分类：人类撰写的证据、参考文献、搜索引擎结果和LLM知识，同时涵盖五个文本生成任务及六个代表性数据集。随后，我们提出UFO，一个基于LLM的统一且灵活的评估框架，用于针对即插即用的事实来源进行事实核验。基于该框架，我们实现了五种评估场景。实验结果表明，对于大多数问答任务，人类撰写的证据和参考文献至关重要，并且在检索增强型问答任务中二者可以相互替代；在新闻事实生成任务中，搜索引擎结果和LLM知识不可或缺。我们的数据集和代码可在https://github.com/WaldenRUC/UFO获取。
英文摘要：
Large language models (LLMs) may generate text that lacks consistency with human knowledge, leading to factual inaccuracies or \textit{hallucination}. Existing research for evaluating the factuality of LLMs involves extracting fact claims using an LLM and verifying them against a predefined fact source. However, these evaluation metrics are task-specific, and not scalable, and the substitutability of fact sources in different tasks is under-explored. To address these challenges, we categorize four available fact sources: human-written evidence, reference documents, search engine results, and LLM knowledge, along with five text generation tasks containing six representative datasets. Then, we propose \texttt{UFO}, an LLM-based unified and flexible evaluation framework to verify facts against plug-and-play fact sources. We implement five evaluation scenarios based on this framework. Experimental results show that for most QA tasks, human-written evidence and reference documents are crucial, and they can substitute for each other in retrieval-augmented QA tasks. In news fact generation tasks, search engine results and LLM knowledge are essential. Our dataset and code are available at \url{https://github.com/WaldenRUC/UFO}.
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。
