LLM Hallucination Reasoning with Zero-shot Knowledge Test

• URL: http://arxiv.org/abs/2411.09689v1
• Date: Thu, 14 Nov 2024 18:55:26 GMT
• Title: LLM Hallucination Reasoning with Zero-shot Knowledge Test
• Authors: Seongmin Lee, Hsiang Hsu, Chun-Fu Chen,
• Abstract summary: We introduce a new task, Hallucination Reasoning, which classifies LLM-generated text into one of three categories: aligned, misaligned, and fabricated. Our experiments conducted on new datasets demonstrate the effectiveness of our method in hallucination reasoning.
• Score: 10.306443936136425
• License:
• Abstract: LLM hallucination, where LLMs occasionally generate unfaithful text, poses significant challenges for their practical applications. Most existing detection methods rely on external knowledge, LLM fine-tuning, or hallucination-labeled datasets, and they do not distinguish between different types of hallucinations, which are crucial for improving detection performance. We introduce a new task, Hallucination Reasoning, which classifies LLM-generated text into one of three categories: aligned, misaligned, and fabricated. Our novel zero-shot method assesses whether LLM has enough knowledge about a given prompt and text. Our experiments conducted on new datasets demonstrate the effectiveness of our method in hallucination reasoning and underscore its importance for enhancing detection performance.

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