Summary of Do Llm Agents Have Regret? a Case Study in Online Learning and Games, by Chanwoo Park et al.
Do LLM Agents Have Regret? A Case Study in Online Learning and Games
by Chanwoo Park, Xiangyu Liu, Asuman Ozdaglar, Kaiqing Zhang
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT)
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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 Large language models (LLMs) have been applied to interactive decision-making through autonomous agents. Despite their success, the performance of LLM agents has not been thoroughly investigated using quantitative metrics, particularly in multi-agent settings where they interact with each other. Our study proposes investigating the interactions of LLM agents in online learning and game theory benchmark settings using the regret metric. We empirically examine no-regret behaviors of LLMs in non-stationary online learning problems and repeated games. Additionally, we provide theoretical insights into the no-regret behaviors under assumptions on supervised pre-training and human decision-maker data generation. Interestingly, we identify cases where advanced LLMs like GPT-4 fail to be no-regret. To promote no-regret behaviors, we propose an unsupervised training loss, regret-loss, which does not require labeled actions. We establish a statistical guarantee for generalization bound and optimization guarantee for minimizing regret-loss, which may lead to known no-regret learning algorithms. Our experiments demonstrate the effectiveness of regret-loss, particularly in addressing “regrettable” cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about how big language models are used in decision-making when humans interact with them. We want to see if these models can make good decisions by themselves or with other models. Right now, we don’t have a clear picture of how well they do this. The researchers looked at how well the models perform in different situations and found some surprising results. They also came up with new ways to train these models so they can make better decisions. This could be important for things like online learning and games. |
Keywords
* Artificial intelligence * Generalization * Gpt * Online learning * Optimization * Supervised * Unsupervised
