Can large language models explore in-context?

Akshay Krishnamurthy, Keegan Harris, Dylan J Foster, Cyril Zhang, Aleksandrs Slivkins

Track: long paper (up to 8 pages)

Keywords: in-context learning, exploration, multi-armed bandits

TL;DR: We investigate the extent to which contemporary LLMs can engage in exploration, a core capability in RL and decision making.

Abstract: We investigate the extent to which contemporary Large Language Models (LLMs) can engage in exploration, a core capability in reinforcement learning and decision making. We focus on native performance of existing LLMs, without training interventions. We deploy LLMs as agents in simple multi-armed bandit environments, specifying the environment description and interaction history entirely in-context, i.e. within the LLM prompt. We experiment with GPT3.5, GPT4, and Llama2, using a variety of prompt designs, and find that the models do not robustly engage in exploration without substantial interventions: i) Across all of our experiments, only one configuration resulted in satisfactory exploratory behavior: GPT4 with chain-of-thought reasoning and an externally summarized interaction history, presented as sufficient statistics; ii) All other configurations did not result in robust exploratory behavior, including those with chain-of-thought reasoning but unsummarized history. Although these findings can be interpreted positively, they suggest that external summarization---which may not be possible in more complex settings---is important for obtaining desirable behavior from LLM agents. We conclude that non-trivial algorithmic interventions, such as fine-tuning or dataset curation, may be required to empower LLM-based decision making agents in complex settings.

Submission Number: 1