arXiv:2606.32008v1 Announce Type: new Abstract: Mechanistic interpretability (MI) requires full access to model internals, yet the APIs for most widely deployed language models at best expose log-probabilities over output tokens. This creates a surrogate problem: when do measurements made on open models allow us to make claims about a closed model? We evaluate surrogate fidelity at the prediction, attribution, and representation levels. For binary classification tasks, log-odds provide an API-compatible scalar readout of the model's representation space, and leave-one-out attributions provide
The proliferation of powerful closed-source large language models and the increasing demand for transparency and interpretability in AI systems is driving research into methods for understanding their behavior.
Sophisticated actors need to understand the limitations and capabilities of closed-source AI models, especially for critical applications where interpretability and trust are paramount.
This research provides a framework for assessing when insights gained from open models can be reliably extrapolated to closed models, impacting development, deployment, and regulatory approaches.
- · AI Interpretabiliy Researchers
- · Organizations deploying Closed-Source LLMs
- · Open-source AI Community
- · AI Ethics & Safety Advocates
- · Closed-Source LLM Developers resistant to transparency
- · Overly simplistic black-box AI deployments
Improved methods for interpreting the behavior and limitations of proprietary large language models become available.
Increased trust and auditability of closed-source AI systems, potentially leading to wider adoption in sensitive domains.
Regulatory frameworks begin to incorporate requirements for explainability assessments of AI models, possibly favoring approaches that leverage surrogate fidelity.
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Read at arXiv cs.LG