Papers › TrustScore: Reference-Free Evaluation of LLM Response Trustworthiness

TrustScore: Reference-Free Evaluation of LLM Response Trustworthiness

19 Feb 2024arXiv:2402.12545archive 2025-07-28

Danna Zheng, Danyang Liu, Mirella Lapata, Jeff Z. Pan

Large Language Models (LLMs) have demonstrated impressive capabilities across various domains, prompting a surge in their practical applications. However, concerns have arisen regarding the trustworthiness of LLMs outputs, particularly in closed-book question-answering tasks, where non-experts may struggle to identify inaccuracies due to the absence of contextual or ground truth information. This paper introduces TrustScore, a framework based on the concept of Behavioral Consistency, which evaluates whether an LLMs response aligns with its intrinsic knowledge. Additionally, TrustScore can seamlessly integrate with fact-checking methods, which assesses alignment with external knowledge sources. The experimental results show that TrustScore achieves strong correlations with human judgments, surpassing existing reference-free metrics, and achieving results on par with reference-based metrics.

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string_matching dannalily/trustscore/distractor_generator.py official repository ran · violated contract MIT (permissive) · d25c90187e2b3ac7 · report

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