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 to sequential decision making. Existing efforts either (i) re-train or finetune LLMs for decision making%2C or (ii) design prompts for pretrained LLMs. The former approach suffers from the computational burden of gradient updates%2C and the latter approach does not show promising results. In this paper%2C we propose a new approach that leverages online model selection algorithms to efficiently incorporate LLMs agents into sequential decision making. Statistically%2C our approach significantly outperforms both traditional decision making algorithms and vanilla LLM agents. Computationally%2C our approach avoids the need for expensive gradient updates of LLMs%2C and throughout the decision making process%2C it requires only a small number of LLM calls. We conduct extensive experiments to verify the effectiveness of our proposed approach. As an example%2C on a large-scale Amazon dataset%2C our approach achieves more than a %246%24x performance gain over baselines while calling LLMs in only %241.5%24%25 of the time steps.?quality=80&w=800)
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