Can LLMs Detect Their Own Hallucinations?

Sora Kadotani; Kosuke Nishida; Kyosuke Nishida

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arXiv · 2511.11087

Can LLMs Detect Their Own Hallucinations?

Sora Kadotani·Kosuke Nishida·Kyosuke Nishida

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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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Sora Kadotani, Kosuke Nishida, Kyosuke Nishida. 2025-11-14. Can LLMs Detect Their Own Hallucinations?. https://arxiv.org/abs/2511.11087

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