Summary of Openfactcheck: Building, Benchmarking Customized Fact-checking Systems and Evaluating the Factuality Of Claims and Llms, by Yuxia Wang et al.
OpenFactCheck: Building, Benchmarking Customized Fact-Checking Systems and Evaluating the Factuality of Claims and LLMs
by Yuxia Wang, Minghan Wang, Hasan Iqbal, Georgi Georgiev, Jiahui Geng, Preslav Nakov
First submitted to arxiv on: 9 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a unified framework called OpenFactCheck for building customized automatic fact-checking systems, benchmarking their accuracy, and evaluating the factual accuracy of large language models (LLMs). The framework consists of three modules: CUSTCHECKER, LLMEVAL, and CHECKEREVAL. CUSTCHECKER allows users to customize an automatic fact-checker and verify document claims, while LLMEVAL provides a unified evaluation framework for assessing LLMs’ factuality from various perspectives fairly. CHECKEREVAL is an extensible solution for evaluating the reliability of automatic fact-checkers’ verification results using human-annotated datasets. The paper aims to address the difficulties in verifying the factual accuracy of LLMs’ outputs, especially in open domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special tool called OpenFactCheck to help machines understand if what they’re saying is true or not. Right now, it’s hard to check how accurate big language models are because different researchers use different ways to measure their accuracy. This makes it hard to compare and improve these models. The OpenFactCheck tool has three parts: one helps users create their own fact-checker, another compares how well different language models can tell facts from fiction, and the last part checks if a machine’s fact-checking results are correct or not. |
