SelfCheckAgent: Zero-Resource Hallucination Detection in Generative Large Language Models

• URL: http://arxiv.org/abs/2502.01812v1
• Date: Mon, 03 Feb 2025 20:42:32 GMT
• Title: SelfCheckAgent: Zero-Resource Hallucination Detection in Generative Large Language Models
• Authors: Diyana Muhammed, Gollam Rabby, Sören Auer,
• Abstract summary: SelfCheckAgent is a novel framework integrating three different agents.<n>These agents provide a robust multi-dimensional approach to hallucination detection.<n>The framework also incorporates a triangulation strategy, which increases the strengths of the SelfCheckAgent.
• Score: 0.16385815610837165
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting hallucinations in Large Language Models (LLMs) remains a critical challenge for their reliable deployment in real-world applications. To address this, we introduce SelfCheckAgent, a novel framework integrating three different agents: the Symbolic Agent, the Specialized Detection Agent, and the Contextual Consistency Agent. These agents provide a robust multi-dimensional approach to hallucination detection. Notable results include the Contextual Consistency Agent leveraging Llama 3.1 with Chain-of-Thought (CoT) to achieve outstanding performance on the WikiBio dataset, with NonFactual hallucination detection scoring 93.64%, Factual 70.26%, and Ranking 78.48% respectively. On the AIME dataset, GPT-4o with CoT excels in NonFactual detection with 94.89% but reveals trade-offs in Factual with 30.58% and Ranking with 30.68%, underscoring the complexity of hallucination detection in the complex mathematical domains. The framework also incorporates a triangulation strategy, which increases the strengths of the SelfCheckAgent, yielding significant improvements in real-world hallucination identification. The comparative analysis demonstrates SelfCheckAgent's applicability across diverse domains, positioning it as a crucial advancement for trustworthy LLMs. These findings highlight the potentiality of consistency-driven methodologies in detecting hallucinations in LLMs.

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