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| Autori principali: | , , , |
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| Natura: | Preprint |
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2024
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| Accesso online: | https://arxiv.org/abs/2402.14690 |
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| _version_ | 1866913240308514816 |
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| author | Huang, Zhaoheng Dou, Zhicheng Zhu, Yutao Wen, Ji-rong |
| author_facet | Huang, Zhaoheng Dou, Zhicheng Zhu, Yutao Wen, Ji-rong |
| contents | 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}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14690 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | UFO: a Unified and Flexible Framework for Evaluating Factuality of Large Language Models Huang, Zhaoheng Dou, Zhicheng Zhu, Yutao Wen, Ji-rong Computation and Language 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}. |
| title | UFO: a Unified and Flexible Framework for Evaluating Factuality of Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2402.14690 |