UFO: a Unified and Flexible Framework for Evaluating Factuality of Large Language Models
<|reference_start|>UFO: a Unified and Flexible Framework for Evaluating Factuality of Large Language Models: 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}.<|reference_end|>
@article{huang2024ufo:,
    title={UFO: a Unified and Flexible Framework for Evaluating Factuality of Large
  Language Models},
    author={Zhaoheng Huang, Zhicheng Dou, Yutao Zhu, Ji-rong Wen},
    journal={arXiv preprint arXiv:2402.14690},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.14690},
    primaryClass={cs.CL}
}