Can LLMs Detect Their Own Hallucinations?

• URL: http://arxiv.org/abs/2511.11087v1
• Date: Fri, 14 Nov 2025 09:03:09 GMT
• Title: Can LLMs Detect Their Own Hallucinations?
• Authors: Sora Kadotani, Kosuke Nishida, Kyosuke Nishida,
• Abstract summary: Large language models (LLMs) can generate fluent responses, but sometimes hallucinate facts.<n>We formulate hallucination detection as a classification task of a sentence.<n>We propose a framework for estimating LLMs' capability of hallucination detection and a classification method using Chain-of-Thought (CoT) to extract knowledge from their parameters.
• Score: 13.071420274309551
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) can generate fluent responses, but sometimes hallucinate facts. In this paper, we investigate whether LLMs can detect their own hallucinations. We formulate hallucination detection as a classification task of a sentence. We propose a framework for estimating LLMs' capability of hallucination detection and a classification method using Chain-of-Thought (CoT) to extract knowledge from their parameters. The experimental results indicated that GPT-$3.5$ Turbo with CoT detected $58.2\%$ of its own hallucinations. We concluded that LLMs with CoT can detect hallucinations if sufficient knowledge is contained in their parameters.

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