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Computer Science > Computation and Language

arXiv:2411.01747 (cs)
[Submitted on 4 Nov 2024]

Title:DynaSaur: Large Language Agents Beyond Predefined Actions

Authors:Dang Nguyen, Viet Dac Lai, Seunghyun Yoon, Ryan A. Rossi,
Handong Zhao, Ruiyi Zhang, Puneet Mathur, Nedim Lipka, Yu Wang, Trung
Bui, Franck Dernoncourt, Tianyi Zhou
View a PDF of the paper titled DynaSaur: Large Language Agents Beyond
Predefined Actions, by Dang Nguyen and 11 other authors
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    Abstract:Existing LLM agent systems typically select actions from
    a fixed and predefined set at every step. While this approach is
    effective in closed, narrowly-scoped environments, we argue that
    it presents two major challenges when deploying LLM agents in
    real-world scenarios: (1) selecting from a fixed set of actions
    significantly restricts the planning and acting capabilities of
    LLM agents, and (2) this approach requires substantial human
    effort to enumerate and implement all possible actions, which
    becomes impractical in complex environments with a vast number of
    potential actions. In this work, we propose an LLM agent
    framework that enables the dynamic creation and composition of
    actions in an online manner. In this framework, the agent
    interacts with the environment by generating and executing
    programs written in a general-purpose programming language at
    each step. Furthermore, generated actions are accumulated over
    time for future reuse. Our extensive experiments on the GAIA
    benchmark demonstrate that this framework offers significantly
    greater flexibility and outperforms previous methods. Notably, it
    allows an LLM agent to recover in scenarios where no relevant
    action exists in the predefined set or when existing actions fail
    due to unforeseen edge cases. At the time of writing, we hold the
    top position on the GAIA public leaderboard. Our code can be
    found in \href{this https URL}{this https URL}.

Comments: 15 pages, 8 figures
Subjects: Computation and Language (cs.CL)
Cite as:  arXiv:2411.01747 [cs.CL]
          (or arXiv:2411.01747v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2411.01747
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Dang Nguyen [view email]
[v1] Mon, 4 Nov 2024 02:08:59 UTC (451 KB)
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