Summary of Can Llms Replace Economic Choice Prediction Labs? the Case Of Language-based Persuasion Games, by Eilam Shapira et al.
Can LLMs Replace Economic Choice Prediction Labs? The Case of Language-based Persuasion Games
by Eilam Shapira, Omer Madmon, Roi Reichart, Moshe Tennenholtz
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Science and Game Theory (cs.GT); Human-Computer Interaction (cs.HC)
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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 Medium Difficulty summary: This paper investigates the ability of Large Language Models (LLMs) to generate training data for predicting human choice in complex economic settings, such as language-based persuasion games. The authors explore whether LLM-generated data can be used to train models that accurately predict human behavior, and surprisingly find that models trained on this synthetic data can outperform those trained on actual human data. This breakthrough has significant implications for applications in marketing, finance, public policy, and more, enabling the development of more efficient and effective decision-making systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine being able to predict how people will make decisions based on words and persuasion. That’s what this paper is all about! Researchers wanted to see if special computer models (LLMs) could help create fake training data that can teach other computers to make these predictions. They tested it in a game where people try to convince each other, and surprisingly found that the LLM-generated data helped the computers make even better predictions than using real human data! |
Keywords
* Artificial intelligence * Synthetic data
