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Calibration

20 papers

Papers per year

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Papers

Demystifying Uncertainty in LLMs: Active Calibration between Concepts and Human Evaluations ACL 2026
The Confidence Dichotomy: Analyzing and Mitigating Miscalibration in Tool-Use Agents ACL 2026
DisCal: Distribution-Aware Calibration for Mathematical Reasoning Under Character-Level Noisy Inputs ACL 2026
Confidence Should Be Calibrated More Than One Turn Deep ACL 2026
When High Accuracy Hides Poor Calibration: Rethinking Confidence Evaluation in Transformer-Based Text Classification with Balanced Brier Score ACL 2026
CARE: Confidence-aware Ratio Estimation for Medical Biomarkers MIDL 2026
A No Free Lunch Theorem for Human-AI Collaboration AAAI 2025
Towards Objective Fine-tuning: How LLMs’ Prior Knowledge Causes Potential Poor Calibration? ACL 2025
Task Calibration: Calibrating Large Language Models on Inference Tasks ACL 2025
Beyond Averages: Learning with Annotator Disagreement in STS EMNLP 2025
Clinical Measurements with Calibrated Instance-Dependent Confidence Interval MIDL 2025
When is Multicalibration Post-Processing Necessary? NIPS 2024
Active, anytime-valid risk controlling prediction sets NIPS 2024
Teaching LLMs to Abstain across Languages via Multilingual Feedback EMNLP 2024
On Diversified Preferences of Large Language Model Alignment EMNLP 2024
Transferable Post-hoc Calibration on Pretrained Transformers in Noisy Text Classification AAAI 2023
On the Calibration of Massively Multilingual Language Models EMNLP 2022
Improving Calibration through the Relationship with Adversarial Robustness NIPS 2021
On the Effects of Transformer Size on In- and Out-of-Domain Calibration EMNLP 2021
Selective Question Answering under Domain Shift ACL 2020