Faithfulness of LLM Self-Explanations for Commonsense Tasks: Larger Is Better, and Instruction-Tuning Allows Trade-Offs but Not Pareto Dominance

• URL: http://arxiv.org/abs/2503.13445v1
• Date: Mon, 17 Mar 2025 17:59:39 GMT
• Title: Faithfulness of LLM Self-Explanations for Commonsense Tasks: Larger Is Better, and Instruction-Tuning Allows Trade-Offs but Not Pareto Dominance
• Authors: Noah Y. Siegel, Nicolas Heess, Maria Perez-Ortiz, Oana-Maria Camburu,
• Abstract summary: We conduct a comprehensive counterfactual faithfulness analysis across 62 models from 8 families.<n>We find that observed differences in faithfulness can often be attributed to explanation verbosity.<n>Our analysis highlights the nuanced relationship between instruction-tuning, verbosity, and the faithful representation of model decision processes.
• Score: 24.1445130682289
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As large language models (LLMs) become increasingly capable, ensuring that their self-generated explanations are faithful to their internal decision-making process is critical for safety and oversight. In this work, we conduct a comprehensive counterfactual faithfulness analysis across 62 models from 8 families, encompassing both pretrained and instruction-tuned variants and significantly extending prior studies of counterfactual tests. We introduce phi-CCT, a simplified variant of the Correlational Counterfactual Test, which avoids the need for token probabilities while explaining most of the variance of the original test. Our findings reveal clear scaling trends: larger models are consistently more faithful on our metrics. However, when comparing instruction-tuned and human-imitated explanations, we find that observed differences in faithfulness can often be attributed to explanation verbosity, leading to shifts along the true-positive/false-positive Pareto frontier. While instruction-tuning and prompting can influence this trade-off, we find limited evidence that they fundamentally expand the frontier of explanatory faithfulness beyond what is achievable with pretrained models of comparable size. Our analysis highlights the nuanced relationship between instruction-tuning, verbosity, and the faithful representation of model decision processes.

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