ArticleFrontiers in artificial intelligence2025
Moving LLM evaluation forward: lessons from human judgment research.
Article in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- A benchmark for assessing large language models on molecular-to-food and food-to-molecular prediction tasks.Frontiers in artificial intelligence · 2026Article
- Editorial: Advanced integration of large language models for autonomous systems and critical decision support.Frontiers in artificial intelligence · 2026Article
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Authors and funding
1 author.
Funding
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Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.