ArticleOpen mind : discoveries in cognitive science2024
Large Language Models and the Wisdom of Small Crowds.
Article in Open mind : discoveries in cognitive science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Contextualized sensorimotor norms: Multi-dimensional measures of sensorimotor strength for ambiguous English words, in context.Behavior research methods · 2026Article
- Adding LLMs to the psycholinguistic norming toolbox: A practical guide to getting the most out of human ratings.Behavior research methods · 2026Article
- Metacognition of ChatGPT in confidence judgements.Frontiers in artificial intelligence · 2026Article
- Kalimah norms: Ratings for 2,467 modern standard Arabic words on two scales.Behavior research methods · 2025Article
- Turing Jest: Distributional Semantics and One-Line Jokes.Cognitive science · 2025Article
- AI can outperform humans in predicting correlations between personality items.Communications psychology · 2025Article
- How should the advancement of large language models affect the practice of science?Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Moving beyond word frequency based on tally counting: AI-generated familiarity estimates of words and phrases are an interesting additional index of language knowledge.Behavior research methods · 2024Article
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Abstract
Recent advances in Large Language Models (LLMs) have raised the question of replacing human subjects with LLM-generated data. While some believe that LLMs capture the "wisdom of the crowd"-due to their vast training data-empirical evidence for this hypothesis remains scarce. We present a novel methodological framework to test this: the "number needed to beat" (NNB), which measures how many humans are needed for a sample's quality to rival the quality achieved by GPT-4, a state-of-the-art LLM. In a series of pre-registered experiments, we collect novel human data and demonstrate the utility of this method for four psycholinguistic datasets for English. We find that NNB > 1 for each dataset, but also that NNB varies across tasks (and in some cases is quite small, e.g., 2). We also introduce two "centaur" methods for combining LLM and human data, which outperform both stand-alone LLMs and human samples. Finally, we analyze the trade-offs in data cost and quality for each approach. While clear limitations remain, we suggest that this framework could guide decision-making about whether and how to integrate LLM-generated data into the research pipeline.
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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.