# Enhancing Assessment of Self-Consistency in LLM Explanations using Perturbation Strength

> Source: <https://www.machinebrief.com/news/enhancing-assessment-of-self-consistency-in-llm-explanations-mhhi>
> Published: 2026-09-28 04:00:00+00:00

arXiv:2609.30849v1 Announce Type: new 
Abstract: Prior work has examined the self-consistency of LLM-generated explanations using surface-level perturbation methods. However, the strength of these perturbations is not explicitly measured and controlled. In this work, we propose an LLM-as-a-judge approach to measure perturbation strength in a unified manner across input and CoT perturbations. We then evaluate the self-consistency in explanations generated from various LLMs under controlled strength conditions, ensuring a fair comparison across perturbation types. Experiments show that our proposed LLM-based perturbation strength measure outperforms other embedding- and probability-based approaches and that input perturbations generally affect LLMs more strongly than CoT perturbations. Our work suggests that judgments about a model's self-consistency is fair only within the same perturbation type.