TrustScore: Reference-Free Evaluation of LLM Response Trustworthiness

• URL: http://arxiv.org/abs/2402.12545v2
• Date: Mon, 6 May 2024 22:02:10 GMT
• Title: TrustScore: Reference-Free Evaluation of LLM Response Trustworthiness
• Authors: Danna Zheng, Danyang Liu, Mirella Lapata, Jeff Z. Pan,
• Abstract summary: Large Language Models (LLMs) have demonstrated impressive capabilities across various domains, prompting a surge in their practical applications. This paper introduces TrustScore, a framework based on the concept of Behavioral Consistency, which evaluates whether an LLMs response aligns with its intrinsic knowledge.
• Score: 58.721012475577716
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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