Enhancing Assessment of Self-Consistency in LLM Explanations using Perturbation Strength
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Computer Science > Computation and Language
Title:Enhancing Assessment of Self-Consistency in LLM Explanations using Perturbation Strength
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.
| Comments: | 22 pages, 10 figures |
| Subjects: | Computation and Language (cs.CL) |
| ACM classes: | I.2.7 |
| Cite as: | arXiv:2609.30849 [cs.CL] |
| (or arXiv:2609.30849v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.30849
arXiv-issued DOI via DataCite (pending registration)
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