Moving LLM evaluation forward: lessons from human judgment research
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 draw...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1592399/full |
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| _version_ | 1849718385776001024 |
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| author | Andrea Polonioli |
| author_facet | Andrea Polonioli |
| author_sort | Andrea Polonioli |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4c8b2134ceaa43bf81d834e0ed3864d2 |
| institution | DOAJ |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-4c8b2134ceaa43bf81d834e0ed3864d22025-08-20T03:12:23ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-05-01810.3389/frai.2025.15923991592399Moving LLM evaluation forward: lessons from human judgment researchAndrea PolonioliThis 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.https://www.frontiersin.org/articles/10.3389/frai.2025.1592399/fullLLMgenerative AI (GenAI)hallucinationsAI in businesshuman judgmentjudgment and decision making |
| spellingShingle | Andrea Polonioli Moving LLM evaluation forward: lessons from human judgment research Frontiers in Artificial Intelligence LLM generative AI (GenAI) hallucinations AI in business human judgment judgment and decision making |
| title | Moving LLM evaluation forward: lessons from human judgment research |
| title_full | Moving LLM evaluation forward: lessons from human judgment research |
| title_fullStr | Moving LLM evaluation forward: lessons from human judgment research |
| title_full_unstemmed | Moving LLM evaluation forward: lessons from human judgment research |
| title_short | Moving LLM evaluation forward: lessons from human judgment research |
| title_sort | moving llm evaluation forward lessons from human judgment research |
| topic | LLM generative AI (GenAI) hallucinations AI in business human judgment judgment and decision making |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1592399/full |
| work_keys_str_mv | AT andreapolonioli movingllmevaluationforwardlessonsfromhumanjudgmentresearch |