Summary of Simulating Field Experiments with Large Language Models, by Yaoyu Chen et al.
Simulating Field Experiments with Large Language Models
by Yaoyu Chen, Yuheng Hu, Yingda Lu
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes and evaluates two prompting strategies to leverage large language models (LLMs) for simulating field experiments. The observer mode allows direct prediction on main conclusions, while the participant mode simulates distributions of responses from participants. Using these approaches, the authors examine 15 well-cited field experimental papers in INFORMS and MISQ, finding encouraging alignments between simulated results and actual results in certain scenarios. They also identify topics where LLMs underperform, such as gender difference and social norms research. The automatic workflow enables large-scale screening of more papers with field experiments. This study pioneers the utilization of LLMs for simulating field experiments, expanding their scope of potential applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to use big language models (LLMs) to simulate real-life experiments. The authors propose two new ways to ask questions that help LLMs make predictions about what will happen in an experiment. They test these approaches on 15 famous studies and find that the predictions are often close to reality. However, they also identify some areas where LLMs struggle, such as understanding gender differences and social norms. This study shows how LLMs can be used to help researchers decide whether to conduct a real-life experiment before investing time and money. |
Keywords
» Artificial intelligence » Prompting
