Summary of Predicting and Understanding Human Action Decisions: Insights From Large Language Models and Cognitive Instance-based Learning, by Thuy Ngoc Nguyen et al.
Predicting and Understanding Human Action Decisions: Insights from Large Language Models and Cognitive Instance-Based Learning
by Thuy Ngoc Nguyen, Kasturi Jamale, Cleotilde Gonzalez
First submitted to arxiv on: 12 Jul 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 The abstract presents a study on Large Language Models (LLMs) and their capabilities in predicting human behavior and biases. Researchers leveraged LLMs’ reasoning and generative abilities to predict human behavior in two sequential decision-making tasks, involving balancing between exploitative and exploratory actions and handling delayed feedback. The paper compares the performance of LLMs with a cognitive instance-based learning (IBL) model, which mimics human experiential decision-making. Results show that LLMs excel at rapidly incorporating feedback to improve prediction accuracy, while the cognitive IBL model better captures human exploratory behaviors and loss aversion bias. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study explores how Large Language Models can predict human behavior and biases. Researchers used these models to make decisions in two tasks, like choosing between different options or handling feedback. They compared the models’ performance with a special kind of learning called instance-based learning, which is similar to how humans make decisions. The results show that the language models are good at using feedback quickly, but the other model does a better job capturing human behavior and biases. |
