arXiv:2607. 20526v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in settings where fluent but incorrect answers can be costly.
By Matthew ffrench-Constant, Daniel Yang, Xinmeng Huang, Sanyam Kapoor
arXiv:2609.24574v1 Announce Type: new
Abstract: Computational social science increasingly relies on large language models for text annotation, and the validity of published findings now rests on the...
By Hazem Ibrahim, Yasir Zaki
The paper argues that calibration—how well a language model’s confidence aligns with its actual correctness—should be a standard evaluation metric for large language models (LLMs). It notes that while calibration metrics exist, they are rarely applied outside specialized NLP subfields, leading to unverified confidence scores in new models, datasets, and benchmarks. The authors highlight the risks of miscalibration both at deployment (overconfident errors causing harm) and in research workflows (affecting LLM-as-a-judge, synthetic data generation, and active learning). They call for every NLP subfield to pair its primary performance metric with a calibration score, treating calibration as an essential property of every model.
By Mario Sanz-Guerrero, Katharina von der Wense
The paper argues that verbalized confidence—once viewed as overconfident and coarse—has become the preferred soft‑scoring method for LLM‑as‑a‑Judge on top‑tier proprietary models released after 2025. Experiments on SummEval, AggreFact, and HelpSteer2 across up to 18 LLMs show that log‑probabilities are no longer the best signal, and that adding an overconfidence advisory and self‑debate further improves calibration and robustness. The authors note that these enhancements incur little accuracy loss on post‑2025 models but do affect pre‑2025 ones, highlighting a compatibility shift in how confidence should be measured.
By Yu-Chung Hsiao
arXiv:2511. 21140v4 Announce Type: replace Abstract: Large language models (LLMs) are widely used as scalable evaluators of model responses in lieu of human annotators.
By Chungpa Lee, Thomas Zeng, Jongwon Jeong, Jy-yong Sohn, Kangwook Lee
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.
By Mohammad Anas Jawad, Cornelia Caragea