Confidence and Calibration of Activation Oracles for Reliable Interpretation of Language Model Internals
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| Authors | Federico Torrielli et al. |
| Year | 2026 |
| HF Upvotes | 10 |
| arXiv | 2605.26045 |
| Download | |
| HF Page | View on Hugging Face |
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 frequency is the best-calibrated method among those tested (ECE 5.7% vs. 25.5% for the answer-word log-probability on Qwen3-8B; 10.3% vs. 13.1% on Qwen3.6-27B), and that the log-prob baseline can serve as a fast triage signal at a fraction of the cost. Code and the patched trainer are available at https://github.com/federicotorrielli/probabilistic_activation_oracles.
Engineering Breakdown
The Problem
However, uncertainty quantification (UQ) for the natural-language outputs of such activation oracles is so far understudied.
The Approach
However, uncertainty quantification (UQ) for the natural-language outputs of such activation oracles is so far understudied.
Key Results
Code and the patched trainer are available at https://github.com/federicotorrielli/probabilistic_activation_oracles.
Research Areas
This paper contributes to the following areas of AI/ML engineering:
- Machine learning
- Deep learning
- Neural networks
- Model optimization
- AI systems
- Confidence
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