arXiv:2605.25052v1 Announce Type: new Abstract: 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
The proliferation of Large Language Models and their integration into critical applications necessitates robust auditing, but current methods are being challenged, creating an urgent need for more reliable interpretability tools.
This research calls into question the very foundation of how we evaluate and trust advanced AI systems, suggesting that current metrics for 'faithfulness' are insufficient and potentially misleading.
The understanding of AI interpretability changes, moving from a reliance on existing faithfulness metrics towards a more rigorous, ground-truth-validated approach, which could impact AI development and regulatory frameworks.
- · AI interpretability researchers
- · AI safety and ethics organizations
- · Developers of new interpretable AI architectures
- · Developers relying solely on current faithfulness metrics
- · Companies marketing 'explainable AI' without rigorous validation
- · Regulatory bodies using flawed interpretability reports
Increased skepticism and scrutiny of AI interpretability claims, leading to a demand for new, validated metrics.
A redirection of research efforts towards developing provably faithful interpretability methods and more rigorous evaluation benchmarks.
Potential delays in the adoption of certain AI applications in high-stakes domains until more trustworthy interpretability solutions are established.
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Read at arXiv cs.CL