PERSPECTIVE article
Front. Artif. Intell.
Sec. AI in Business
Volume 8 - 2025 | doi: 10.3389/frai.2025.1592399
Moving LLM Evaluation Forward: Lessons from Human Judgment Research
Provisionally accepted
- Coveo, Quebec City, Canada
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This paper outlines a path toward more reliable and effective evaluation of Large Language Models (LLMs). It argues that insights from the study of human judgment and decision-making can illuminate current challenges in LLM assessment and help close critical gaps in how models are evaluated. By drawing parallels between human reasoning and model behavior, the paper advocates moving beyond narrow metrics toward more nuanced, ecologically valid frameworks.
Keywords: LLM, generative AI (GenAI), Hallucinations, ai in business, human judgement, Judgement and decision making, Heuristics & biases
Received: 12 Mar 2025; Accepted: 29 Apr 2025.
Copyright: © 2025 Polonioli. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Andrea Polonioli, Coveo, Quebec City, Canada
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.