Faithfulness Metrics Don't Measure Faithfulness: A Meta-Evaluation with Ground Truth

Hugging Face Daily Papers Papers
faithfulness chain-of-thought metrics evaluation benchmark interpretability llm

Summary

This paper introduces BonaFide, a benchmark of 3,066 labeled chain-of-thought examples across 13 tasks and 10 models, and systematically evaluates faithfulness metrics, showing that most perform near chance and have significant limitations in reliability and efficiency.

Chains of thought (CoTs) have become central in interpreting and auditing behaviors of large language models. Yet growing evidence suggests that these traces often fail to faithfully represent the computations behind a model's predictions. Several faithfulness metrics have been proposed, but whether they indeed measure faithfulness remains unknown. Answering this requires ground-truth labels, which are hard to obtain since internal computations are not directly observable. Consequently, most works proposing metrics report only absolute scores or comparisons to prior metrics, and the few existing benchmarks rely on proxies like plausibility or importance, properties orthogonal to faithfulness that can mislead about whether a CoT can be trusted. We address this challenge by constructing tasks whose outputs reveal which intermediate computations must have produced them, and developing an automated labeling pipeline that yields ground-truth faithfulness labels at both the step and CoT level. Building on this methodology, we present BonaFide, a benchmark of 3,066 labeled CoTs across 13 tasks and 10 models, and use it to conduct the first systematic evaluation of prominent faithfulness metrics. Our experiments show that most metrics perform near chance, exhibit strong prediction biases and degrade on longer CoTs. The best metric reaches only 0.70 AUROC at the CoT level while another reaches 0.59 at the step level, with neither transferring across settings, while entailing prohibitively high computational cost. Our results expose fundamental gaps in current faithfulness evaluation and call for the development of more reliable and efficient metrics.
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Source: https://huggingface.co/papers/2605.25052

Abstract

Researchers created a benchmark with 3,066 labeled chains of thought examples across 13 tasks and 10 models to systematically evaluate faithfulness metrics, revealing that most metrics perform near randomly and have significant limitations in reliability and efficiency.

Chains of thought(CoTs) have become central in interpreting and auditing behaviors of large language models. Yet growing evidence suggests that these traces often fail to faithfully represent the computations behind a model’s predictions. Severalfaithfulness metricshave been proposed, but whether they indeed measure faithfulness remains unknown. Answering this requiresground-truth labels, which are hard to obtain since internal computations are not directly observable. Consequently, most works proposing metrics report only absolute scores or comparisons to prior metrics, and the few existing benchmarks rely on proxies like plausibility or importance, properties orthogonal to faithfulness that can mislead about whether a CoT can be trusted. We address this challenge by constructing tasks whose outputs reveal whichintermediate computationsmust have produced them, and developing anautomated labeling pipelinethat yields ground-truth faithfulness labels at both the step and CoT level. Building on this methodology, we present BonaFide, a benchmark of 3,066 labeled CoTs across 13 tasks and 10 models, and use it to conduct the first systematic evaluation of prominentfaithfulness metrics. Our experiments show that most metrics perform near chance, exhibit strongprediction biasesand degrade on longer CoTs. The best metric reaches only 0.70AUROCat the CoT level while another reaches 0.59 at the step level, with neither transferring across settings, while entailing prohibitively high computational cost. Our results expose fundamental gaps in current faithfulness evaluation and call for the development of more reliable and efficient metrics.

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