The paper investigates the "score granularity gap" in black-box large language model (LLM) classifiers, asking how finely a confidence score can be thresholded for deployment. By comparing seven confidence construction methods across 25 model-dataset pairs, the authors find that single-shot verbalized confidence, when properly converted to a probability, ranks well but offers only a few distinct threshold values, limiting operational flexibility. The study also shows that multi-query aggregation can improve weak models but may harm strong ones, and provides concrete guidance for deployment trade-offs.
By Ao Sun, Tian Sun, Jiaxing Geng
arXiv:2608. 19323v1 Announce Type: cross Abstract: Uncertainty quantification (UQ) is essential for the safe deployment of large language models (LLMs).
By Sokhna Diarra Mbacke, Mouloud Belbahri, Gabriel Loaiza-Ganem
arXiv:2605. 15416v2 Announce Type: replace-cross Abstract: Jung et al.
By Gaojie Jin, Yong Tao, Lijia Yu, Tianjin Huang
arXiv:2609.10122v1 Announce Type: new
Abstract: Large language models (LLMs) have achieved strong performance across a broad range of classification settings, yet the reliability of their predictions...
By Jianzong Wang, Chuhang Liu, Botao Zhao, Zuheng Kang, Xulong Zhang, Xiaoyang Qu, Junqing Peng, Zhiewei Ye, Yayun He
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:2607. 03882v1 Announce Type: cross Abstract: LLMs are increasingly deployed as post-hoc explainers of AI-generated outputs, yet it remains unclear whether they can reliably communicate probabilistic information in natural language.
By Diego Cerda-Mardini, Sarath Chandar, Sreenath Madathil
Large language models (LLMs) are increasingly used to evaluate output quality, but guaranteeing agreement with human judgments is difficult. The paper introduces a Localize-Then-Decide framework that first uses conformal prediction to narrow down a shortlist likely to contain the human-preferred response, then applies a calibrated confidence rule to select a single response or abstain. Experiments show this two-stage approach consistently yields higher guarantee success rates and greater coverage than single-stage baselines across various candidate sizes and datasets.
By Xinyu Li, Yi Zhou, Guanqun Cao, Zeyu Fu, Tianjin Huang, Gaojie Jin
arXiv:2606. 05308v1 Announce Type: new Abstract: With PRECISE, we extended Prediction-Powered Inference to produce bias-corrected estimates of ranking evaluation metrics by combining a small human-labeled set with a large LLM-judged set.
By Abhishek Divekar
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:2608.23086v1 Announce Type: new
Abstract: Black-box large language models need confidence scores that can separate likely-correct from likely-incorrect outputs, enabling systems to prioritize h...
By Rounak Sharma, Ananya B. Sai, Soumyabrata Pal
arXiv:2605. 04638v2 Announce Type: replace-cross Abstract: Uncertainty quantification (UQ) is an important technique for ensuring the trustworthiness of LLMs, given their tendency to hallucinate.
By Mingda Li, Rundong Lv, Xinyu Li, Weinan Zhang, Ting Liu
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