Related papers: FactSelfCheck: Fact-Level Black-Box Hallucination Detection for LLMs

FactSelfCheck: Fact-Level Black-Box Hallucination Detection for LLMs

URL: http://arxiv.org/abs/2503.17229v1
Date: Fri, 21 Mar 2025 15:32:24 GMT
Title: FactSelfCheck: Fact-Level Black-Box Hallucination Detection for LLMs
Authors: Albert Sawczyn, Jakub Binkowski, Denis Janiak, Bogdan Gabrys, Tomasz Kajdanowicz,
Abstract summary: Large Language Models (LLMs) frequently generate hallucinated content.<n>We propose FactSelfCheck, a novel black-box sampling-based method that enables fine-grained fact-level detection.<n>Our approach represents text as knowledge graphs consisting of facts in the form of triples.
Score: 8.820670807424174
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Large Language Models (LLMs) frequently generate hallucinated content, posing significant challenges for applications where factuality is crucial. While existing hallucination detection methods typically operate at the sentence level or passage level, we propose FactSelfCheck, a novel black-box sampling-based method that enables fine-grained fact-level detection. Our approach represents text as knowledge graphs consisting of facts in the form of triples. Through analyzing factual consistency across multiple LLM responses, we compute fine-grained hallucination scores without requiring external resources or training data. Our evaluation demonstrates that FactSelfCheck performs competitively with leading sampling-based methods while providing more detailed insights. Most notably, our fact-level approach significantly improves hallucination correction, achieving a 35% increase in factual content compared to the baseline, while sentence-level SelfCheckGPT yields only an 8% improvement. The granular nature of our detection enables more precise identification and correction of hallucinated content.

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