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  • ReAct: Synergizing Reasoning and Acting in Language Models
    We apply our approach, named ReAct, to a diverse set of language and decision making tasks and demonstrate its effectiveness over state-of-the-art baselines, as well as improved human interpretability and trustworthiness over methods without reasoning or acting components
  • ReAct: Synergizing Reasoning and Acting in Language Models - ICLR
    We apply our approach, named ReAct, to a diverse set of language and decision making tasks and demonstrate its effectiveness over state-of-the-art baselines, as well as improved human interpretability and trustworthiness over methods without reasoning or acting components
  • ReAct: Synergizing Reasoning and Acting in Language Models
    This paper proposes ReAct; a novel framework for prompting large language models (LLMs) on tasks that require explicit reasoning and or acting in an environment
  • arXiv:2210. 03629v3 [cs. CL] 10 Mar 2023
    compared to reasoning or acting alone In this work, we present ReAct, a general paradigm to combine reasoning and acting with language models for solving diverse language reasonin
  • \\model: Synergizing Reasoning and Acting in Language Models
    This tight synergy between “acting” and “reasoning” allows humans to learn new tasks quickly and perform robust decision making or reasoning, even under previously unseen circumstances or facing information uncertainties
  • 重读 ReAct 论文： LLM Agent 的开山之作
    这篇论文来自最近 AI 圈的风云人物姚顺雨，它系统性地提出了将 Reasoning 与 Acting 结合的范式，也就是我们现在熟知的 ReAct 模式。 论文： REACT: SYNERGIZING REASONING AND ACTING IN LANGUAGE MODELS
  • ReAct: Synergizing Reasoning and Acting in Language Models
    We apply our approach, named ReAct, to a diverse set of language and decision making tasks and demonstrate its effectiveness over state-of-the-art baselines, as well as improved human interpretability and trustworthiness over methods without reasoning or acting components
  • REACT: SYNERGIZING REASONING AND ACTING IN LANGUAGE MODELS
    We apply our approach, named ReAct, to a diverse set of language and decision making tasks and demonstrate its effectiveness over state-of-the-art baselines in addition to improved human interpretability and trustworthiness


















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