The ACUTE Protocol: Operationalizing Language Model Activations for Better Calibration, Utility, and Trust
As language models improve and become increasingly deployed to solve a variety of tasks, trustworthiness becomes essential. Calibration is a good proxy for trust: well-calibrated confidence estimates help inform the risk versus reward tradeoff when trusting a specific model output. Unfortunately, even as models improve, they remain poorly calibrated, often biasing towards overconfidence. Additionally, calibration can be gamed: a policy that always predicts the base rate is perfectly calibrated, but completely uninformative. To resolve this, we develop a new metric, expected utility renormalized by the oracle (EURO), that balances calibration and informativeness. We also propose a general-...
| Authors | Nishant Subramani, Palash Goyal, Yiwen Song et al. |
|---|---|
| Primary category | cs.CL |
| Published | 5 June 2026 |
| Last updated at source | 17 June 2026 |
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