arXiv AI June 26, 2026 By Kylie Anglin

Estimating Uncertainty in Classifier Performance with Applications to Large Language Models and Nested Data

arXiv:2606. 26422v1 Announce Type: new Abstract: Researchers increasingly use text classification--supervised models or large language models--to measure constructs from natural language, providing metrics such as recall and precision as evidence of their validity.

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

arXiv Machine Learning

Jun 29

Benchmarking on Tasks That Matter: Dataset Selection for Preserving Model Rankings

arXiv:2606. 27997v1 Announce Type: new Abstract: Benchmarks of machine learning models often include many datasets, making evaluation expensive.

By Rostislav Gusev, Alexey Zaytsev

nlp benchmarks

arXiv AI

Jul 23

Rethinking Uncertainty Evaluation in Large Language Models

arXiv:2607. 19367v1 Announce Type: new Abstract: Calibration is the primary criterion for evaluating LLM confidence, but it is insufficient: it admits trivially incoherent estimators, depends on the evaluation distribution, and does not test the extent to which the estimation can be interpreted as a consistent, underlying probability function.

By Krish Matta, Atharv Naphade, Andy Zou

llms reinforcement-learning

arXiv Machine Learning

Jun 2

How to Correctly Report LLM-as-a-Judge Evaluations

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

llms safety

Hugging Face Trending Papers

Aug 5

Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification

Confidence estimation is essential when LLMs are used for classification, indicating when predictions can be trusted. However, common approaches such as verbalization produce extremely sparse outputs.

llms efficiency

Hugging Face Trending Papers

Jul 7

SpanUQ: Span-Level Uncertainty Quantification for Large Language Model Generation

Uncertainty estimation is essential not only for the trustworthy deployment of large language models (LLMs) but also as a foundation for self-refinement in LLM generation. However, existing approaches operate at suboptimal granularities: token-level scores lack semantic coherence, while sequence-level scores fail to localize errors.

llms benchmarks

arXiv AI

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.

By Matthew ffrench-Constant, Daniel Yang, Xinmeng Huang, Sanyam Kapoor