UFO: a Unified and Flexible Framework for Evaluating Factuality of Large
  Language Models
Zhaoheng Huang Zhicheng Dou Yutao Zhu Ji-rong Wen
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}.