Can large language models explore in-context?
<|reference_start|>Can large language models explore in-context?: 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 GPT-3.5, GPT-4, 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: GPT-4 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.<|reference_end|>
@article{krishnamurthy2024can,
    title={Can large language models explore in-context?},
    author={Akshay Krishnamurthy, Keegan Harris, Dylan J. Foster, Cyril Zhang,
  Aleksandrs Slivkins},
    journal={arXiv preprint arXiv:2403.15371},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15371},
    primaryClass={cs.LG cs.AI cs.CL}
}