Core: Robust Factual Precision with Informative Sub-Claim Identification

• URL: http://arxiv.org/abs/2407.03572v2
• Date: Tue, 15 Oct 2024 21:49:55 GMT
• Title: Core: Robust Factual Precision with Informative Sub-Claim Identification
• Authors: Zhengping Jiang, Jingyu Zhang, Nathaniel Weir, Seth Ebner, Miriam Wanner, Kate Sanders, Daniel Khashabi, Anqi Liu, Benjamin Van Durme,
• Abstract summary: We observe that popular metrics can be manipulated by adding obvious or repetitive subclaims to artificially inflate scores. This observation motivates our new customizable plug-and-play subclaim selection component called Core. We show that many popular factual precision metrics augmented by Core are substantially more robust on a wide range of knowledge domains.
• Score: 44.36892500212747
• License:
• Abstract: Hallucinations pose a challenge to the application of large language models (LLMs) thereby motivating the development of metrics to evaluate factual precision. We observe that popular metrics using the Decompose-Then-Verify framework, such as \FActScore, can be manipulated by adding obvious or repetitive subclaims to artificially inflate scores. This observation motivates our new customizable plug-and-play subclaim selection component called Core, which filters down individual subclaims according to their uniqueness and informativeness. We show that many popular factual precision metrics augmented by Core are substantially more robust on a wide range of knowledge domains. We release an evaluation framework supporting easy and modular use of Core and various decomposition strategies, which we recommend adoption by the community. We also release an expansion of the FActScore biography dataset to facilitate further studies of decomposition-based factual precision evaluation.

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