arXiv:2608. 19323v1 Announce Type: cross Abstract: Uncertainty quantification (UQ) is essential for the safe deployment of large language models (LLMs).
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2504.18346v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have been transformative across many domains. However, hallucination, i.e., confidently outputting incorrect inf...
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.
arXiv:2606. 19868v1 Announce Type: new Abstract: Although large language models (LLMs) have shown strong capabilities across a wide range of tasks, their outputs often remain unreliable and may contain hallucinations, making uncertainty estimation (UE) essential for building trustworthy LLMs.
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...
arXiv:2604.08974v2 Announce Type: replace Abstract: Uncertainty quantification techniques measure confidence in language model outputs to support critical applications like hallucination detection an...
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.