arXiv:2510.18196v2 Announce Type: replace-cross
Abstract: Large Language Models (LLMs) are commonly used as evaluators in various applications, but the reliability of the outcomes remains a challenge. One such challenge is using LLMs-as-judges for direct assessment, i.e., assigning scores from a specified range without any references. Focusing on summarization, we first show that this challenge stems from LLM judge outputs being associated with score range bias, i.e., LLM judge outputs are highly sensitive to pre-defined score ranges. We also show that similar biases exist among models from the same family. We then mitigate this bias through contrastive decoding, achieving up to 11.7% relative improvement on average in Spearman correlation with human judgments across different score ranges.
Assessing nurses’ attitudes toward artificial intelligence in Kazakhstan: psychometric validation of a nine-item scale
BackgroundArtificial intelligence (AI) is increasingly integrated into healthcare, yet the attitudes and knowledge of nurses, who are the key mediators of AI implementation, remain underexplored.