Confidence and Calibration of Activation Oracles for Reliable Interpretation of Language Model Internals
arXiv:2605.26045v1 Announce Type: new Abstract: Activation oracles aim to make the activations of other models legible to humans and yield promising results compared to white-box interpretability techniques. However, uncertainty quantification (UQ) for the natural-language outputs of such activation oracles is so far understudied. Here, we investigate 6 different methods for estimating the confidence of activation oracles and evaluate how well-calibrated their confidence scores are. Our experiments on 6,000 samples per oracle (varying verbalizer and context prompts) reveal that bootstrap mode
As AI models become more complex and integrated into critical systems, the need for reliable interpretation and trust in their internal workings is becoming paramount.
Improving the confidence and calibration of activation oracles is crucial for advancing AI safety and interpretability, enabling more reliable deployment of advanced language models.
The ability to quantify uncertainty in AI model interpretations will enable more robust development and deployment of explainable AI systems.
- · AI Safety Researchers
- · Explainable AI Platforms
- · Developers of interpretability tools
- · Organizations deploying critical AI systems
- · Black-box AI vendors
- · Researchers relying solely on qualitative interpretability
Increased trustworthiness and broader adoption of AI systems in sensitive applications due to better interpretability.
New regulatory requirements for explainability and confidence measures in AI, particularly in sectors like finance and healthcare.
Acceleration of research into novel interpretability techniques and the development of 'self-interpreting' AI architectures.
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