Papers
1–3 of 3Research Paper·Mar 17, 2026
Noise-Response Calibration: A Causal Intervention Protocol for LLM-Judges
Large language models (LLMs) are increasingly used as automated judges and synthetic labelers, especially in low-label settings. Yet these systems are stochastic and often overconfident, which makes d...
7.0 viability
Research Paper·Mar 18, 2026
How do LLMs Compute Verbal Confidence
Verbal confidence -- prompting LLMs to state their confidence as a number or category -- is widely used to extract uncertainty estimates from black-box models. However, how LLMs internally generate su...
5.0 viability
Research Paper·Mar 6, 2026
From Entropy to Calibrated Uncertainty: Training Language Models to Reason About Uncertainty
Large Language Models (LLMs) that can express interpretable and calibrated uncertainty are crucial in high-stakes domains. While methods to compute uncertainty post-hoc exist, they are often sampling-...
3.0 viability