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Do Large Language Model (LLM) Agents Have Regret?
Large Language Models (LLMs) have shown remarkable successes in various applications, including embodied AI, natural science, social science, and solving games. However, their performance in decision-making, especially in multi-agent settings, has not been fully investigated through quantitative metrics.
Studying LLMs’ Decision-Making Behaviors
Researchers from MIT and the University of Maryland propose to study LLMs’ interactions in decision-making settings through the lens of regret. They aim to examine and validate the no-regret behavior of LLMs in benchmark online learning and game settings. The study provides theoretical insights into the no-regret behavior of pre-trained LLMs and identifies cases where they fail to be no-regret.
Practical AI Solutions
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