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        <title>Efficient Sequential Decision Making with Large Language Models</title>
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        <namePart type="given">Dingyang</namePart>
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    <name type="personal">
        <namePart type="given">Yinglun</namePart>
        <namePart type="family">Zhu</namePart>
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            <namePart type="given">Mohit</namePart>
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            <namePart type="given">Yun-Nung</namePart>
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    <abstract>This paper focuses on extending the success of large language models (LLMs) to sequential decision making. Existing efforts either (i) re-train or finetune LLMs for decision making, or (ii) design prompts for pretrained LLMs. The former approach suffers from the computational burden of gradient updates, and the latter approach does not show promising results. In this paper, we propose a new approach that leverages online model selection algorithms to efficiently incorporate LLMs agents into sequential decision making. Statistically, our approach significantly outperforms both traditional decision making algorithms and vanilla LLM agents. Computationally, our approach avoids the need for expensive gradient updates of LLMs, and throughout the decision making process, it requires only a small number of LLM calls. We conduct extensive experiments to verify the effectiveness of our proposed approach. As an example, on a large-scale Amazon dataset, our approach achieves more than a 6x performance gain over baselines while calling LLMs in only 1.5% of the time steps.</abstract>
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        <url>https://aclanthology.org/2024.emnlp-main.517</url>
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%0 Conference Proceedings
%T Efficient Sequential Decision Making with Large Language Models
%A Chen, Dingyang
%A Zhang, Qi
%A Zhu, Yinglun
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F chen-etal-2024-efficient
%X This paper focuses on extending the success of large language models (LLMs) to sequential decision making. Existing efforts either (i) re-train or finetune LLMs for decision making, or (ii) design prompts for pretrained LLMs. The former approach suffers from the computational burden of gradient updates, and the latter approach does not show promising results. In this paper, we propose a new approach that leverages online model selection algorithms to efficiently incorporate LLMs agents into sequential decision making. Statistically, our approach significantly outperforms both traditional decision making algorithms and vanilla LLM agents. Computationally, our approach avoids the need for expensive gradient updates of LLMs, and throughout the decision making process, it requires only a small number of LLM calls. We conduct extensive experiments to verify the effectiveness of our proposed approach. As an example, on a large-scale Amazon dataset, our approach achieves more than a 6x performance gain over baselines while calling LLMs in only 1.5% of the time steps.
%U https://aclanthology.org/2024.emnlp-main.517
%P 9157-9170

Markdown (Informal)

[Efficient Sequential Decision Making with Large Language Models](https://aclanthology.org/2024.emnlp-main.517) (Chen et al., EMNLP 2024)

• Efficient Sequential Decision Making with Large Language Models (Chen et al., EMNLP 2024)

ACL

• Dingyang Chen, Qi Zhang, and Yinglun Zhu. 2024. Efficient Sequential Decision Making with Large Language Models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 9157–9170, Miami, Florida, USA. Association for Computational Linguistics.