LLM Hallucination Reasoning with Zero-shot Knowledge Test
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.
BibTeX
@article{lee2024llm,
title={{LLM} {H}allucination {R}easoning with {Z}ero-shot {K}nowledge {T}est},
author={Lee, Seongmin and Hsu, Hsiang and Chen, Chun-Fu},
journal={arXiv preprint arXiv:2411.09689},
year={2024}
}