arXiv Machine Learning June 5, 2026 By Mohammad Anas Jawad, Cornelia Caragea

CaliDist: Calibrating Large Language Models via Behavioral Robustness to Distraction

Read the original on arXiv Machine Learning →

arXiv:2606. 05799v1 Announce Type: new Abstract: Existing calibration methods for Large Language Models (LLMs) often overlook a critical dimension of trustworthiness: a model's {\em behavioral robustness} to irrelevant or misleading information.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Jul 24

ConfidenceBench: Evaluating Confidence Calibration in Large Language Models

arXiv:2607. 20526v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in settings where fluent but incorrect answers can be costly.

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Aug 6

Provable Limits and Certified Deferral for Verbalized Uncertainty in Small Language Models

arXiv:2608. 05064v1 Announce Type: cross Abstract: Small open-weight language models increasingly run in private, offline, and cost-sensitive settings, where the key deployment question is not only what a model answers but when it should defer to a human.

By Jianru Shen

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Jun 9

The ACUTE Protocol: Operationalizing Language Model Activations for Better Calibration, Utility, and Trust

arXiv:2606. 07822v1 Announce Type: cross Abstract: As language models improve and become increasingly deployed to solve a variety of tasks, trustworthiness becomes essential.

By Nishant Subramani, Palash Goyal, Yiwen Song, Mani Malek, Yuan Xue, Tomas Pfister, Hamid Palangi

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Stable Miscalibration in Large Language Models: A Practical View of High-Confidence Errors

arXiv:2608. 13591v1 Announce Type: new Abstract: High-confidence errors in large language models are often treated as evidence of fragile internal inference.

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Calibration Drift Under Reasoning: How Chain-of-Thought Budgets Induce Overconfidence in Large Language Models

arXiv:2606. 11211v1 Announce Type: cross Abstract: The ability of large language models (LLMs) to express calibrated uncertainty is important for safe deployment.

By Prakul Sunil Hiremath, Harshit R. Hiremath

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arXiv Machine Learning

Jun 25

Don't Go Breaking My LLM: The Impact of Pruning Attention Layers on Explanation Faithfulness and Confidence Calibration

arXiv:2606. 24970v1 Announce Type: new Abstract: Pruning Large Language Models (LLMs) reduces memory and inference costs by removing parts of the network, producing smaller models that retain most of their accuracy.

By Pietro Tropeano, Maria Maistro, Tuukka Ruotsalo, Christina Lioma

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ConfidenceBench: Evaluating Confidence Calibration in Large Language Models

Provable Limits and Certified Deferral for Verbalized Uncertainty in Small Language Models

The ACUTE Protocol: Operationalizing Language Model Activations for Better Calibration, Utility, and Trust

Stable Miscalibration in Large Language Models: A Practical View of High-Confidence Errors

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Don't Go Breaking My LLM: The Impact of Pruning Attention Layers on Explanation Faithfulness and Confidence Calibration