Faithfulness Metrics Don't Measure Faithfulness: A Meta-Evaluation with Ground Truth
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 faithfuln...
| Authors | Yoav Gur-Arieh, Ana Marasović, Mor Geva |
|---|---|
| Primary category | cs.CL |
| Published | 24 May 2026 |
| Last updated at source | 24 May 2026 |
Models and datasets referencing this paper
- yoavgurarieh/BonaFide-ExtendedDataset
- yoavgurarieh/BonaFideDataset
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