PreAct: Predicting Future in ReAct Enhances Agent's Planning Ability
Dayuan Fu Jianzhao Huang Siyuan Lu Guanting Dong Yejie Wang Keqing He Weiran Xu
Addressing the discrepancies between predictions and actual outcomes often
aids individuals in expanding their thought processes and engaging in
reflection, thereby facilitating reasoning in the correct direction. In this
paper, we introduce $\textbf{PreAct}$, an agent framework that integrates
$\textbf{pre}$diction with $\textbf{rea}$soning and $\textbf{act}$ion.
Leveraging the information provided by predictions, a large language model
(LLM) based agent can offer more diversified and strategically oriented
reasoning, which in turn leads to more effective actions that help the agent
complete complex tasks. Our experiments demonstrate that PreAct outperforms the
ReAct approach in accomplishing complex tasks and that PreAct can be
co-enhanced when combined with Reflexion methods. We prompt the model with
different numbers of historical predictions and find that historical
predictions have a sustained positive effect on LLM planning. The differences
in single-step reasoning between PreAct and ReAct show that PreAct indeed
offers advantages in terms of diversity and strategic directivity over ReAct.