ArticleScience advances2024
Wisdom of the silicon crowd: LLM ensemble prediction capabilities rival human crowd accuracy.
Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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.
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Who cites it
10 citing papers in PubMed.
- Similar aggregate but divergent disaggregate responses in human and AI under sequential reward-penalty incentives.iScience · 2026Article
- Six misconceptions about large language models: A minimal model and diagnostic taxonomy.PNAS nexus · 2026Article
- Fixing Work, Not Workers: Leveraging Artificial Intelligence to Combat Burnout in Canadian Healthcare.Healthcare management forum · 2026Article
- Simulating population compliance with pandemic interventions using large language models.medRxiv : the preprint server for health sciences · 2026Article
- Integration of LLMs and VLMs in plant stress phenotyping: From trait recognition to decision support.Plant phenomics (Washington, D.C.) · 2026Review
- Article
- Exploring the potential of large language models in nutrition behavior prediction: evidence from college students.Frontiers in nutrition · 2026Article
- Comparing AI and human decision-making mechanisms in daily collaborative experiments.iScience · 2025Article
- AI can outperform humans in predicting correlations between personality items.Communications psychology · 2025Article
- Can large language models help predict results from a complex behavioural science study?Royal Society open science · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human forecasting accuracy improves through the "wisdom of the crowd" effect, in which aggregated predictions tend to outperform individual ones. Past research suggests that individual large language models (LLMs) tend to underperform compared to human crowd aggregates. We simulate a wisdom of the crowd effect with LLMs. Specifically, we use an ensemble of 12 LLMs to make probabilistic predictions about 31 binary questions, comparing them with those made by 925 human forecasters in a 3-month tournament. We show that the LLM crowd outperforms a no-information benchmark and is statistically indistinguishable from the human crowd. We also observe human-like biases, such as the acquiescence bias. In another study, we find that LLM predictions (of GPT-4 and Claude 2) improve when exposed to the median human prediction, increasing accuracy by 17 to 28%. However, simply averaging human and machine forecasts yields more accurate results. Our findings suggest that LLM predictions can rival the human crowd's forecasting accuracy through simple aggregation.
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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.