Related papers: A novel hallucination classification framework

A novel hallucination classification framework

URL: http://arxiv.org/abs/2510.05189v1
Date: Mon, 06 Oct 2025 09:54:20 GMT
Title: A novel hallucination classification framework
Authors: Maksym Zavhorodnii, Dmytro Dehtiarov, Anna Konovalenko,
Abstract summary: This work introduces a novel methodology for the automatic detection of hallucinations generated during large language model (LLM) inference.<n>The proposed approach is based on a systematic taxonomy and controlled reproduction of diverse hallucination types through prompt engineering.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: This work introduces a novel methodology for the automatic detection of hallucinations generated during large language model (LLM) inference. The proposed approach is based on a systematic taxonomy and controlled reproduction of diverse hallucination types through prompt engineering. A dedicated hallucination dataset is subsequently mapped into a vector space using an embedding model and analyzed with unsupervised learning techniques in a reduced-dimensional representation of hallucinations with veridical responses. Quantitative evaluation of inter-centroid distances reveals a consistent correlation between the severity of informational distortion in hallucinations and their spatial divergence from the cluster of correct outputs. These findings provide theoretical and empirical evidence that even simple classification algorithms can reliably distinguish hallucinations from accurate responses within a single LLM, thereby offering a lightweight yet effective framework for improving model reliability.

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