AutoHall: Automated Hallucination Dataset Generation for Large Language Models

• URL: http://arxiv.org/abs/2310.00259v2
• Date: Fri, 19 Jul 2024 11:48:21 GMT
• Title: AutoHall: Automated Hallucination Dataset Generation for Large Language Models
• Authors: Zouying Cao, Yifei Yang, Hai Zhao,
• Abstract summary: This paper introduces a method for automatically constructing model-specific hallucination datasets based on existing fact-checking datasets called AutoHall. We also propose a zero-resource and black-box hallucination detection method based on self-contradiction.
• Score: 56.92068213969036
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While Large language models (LLMs) have garnered widespread applications across various domains due to their powerful language understanding and generation capabilities, the detection of non-factual or hallucinatory content generated by LLMs remains scarce. Currently, one significant challenge in hallucination detection is the laborious task of time-consuming and expensive manual annotation of the hallucinatory generation. To address this issue, this paper first introduces a method for automatically constructing model-specific hallucination datasets based on existing fact-checking datasets called AutoHall. Furthermore, we propose a zero-resource and black-box hallucination detection method based on self-contradiction. We conduct experiments towards prevalent open-/closed-source LLMs, achieving superior hallucination detection performance compared to extant baselines. Moreover, our experiments reveal variations in hallucination proportions and types among different models.

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