Large Language Models (LLMs) are increasingly being used for interactive
decision-making tasks requiring planning and adapting to the environment.
Recent works employ LLMs-as-agents in broadly two ways: iteratively determining
the next action (iterative executors) or generating plans and executing
sub-tasks using LLMs (plan-and-execute). However, these methods struggle with
task complexity, as the inability to execute any sub-task may lead to task
failure. To address these shortcomings, we introduce As-Needed Decomposition
and Planning for complex Tasks (ADaPT), an approach that explicitly plans and
decomposes complex sub-tasks as-needed, i.e., when the LLM is unable to execute
them. ADaPT recursively decomposes sub-tasks to adapt to both task complexity
and LLM capability. Our results demonstrate that ADaPT substantially
outperforms established strong baselines, achieving success rates up to 28.3%
higher in ALFWorld, 27% in WebShop, and 33% in TextCraft -- a novel
compositional dataset that we introduce. Through extensive analysis, we
illustrate the importance of multilevel decomposition and establish that ADaPT
dynamically adjusts to the capabilities of the executor LLM as well as to task
complexity.

大型语言模型（LLMs）在交互决策任务中的规划和适应环境方面的应用，遇到任务复杂性挑战时，我们通过 ADaPT 方法明确计划和分解复杂子任务，在多层次分解中动态调整执行者 LLM 的能力以及任务复杂性，最终取得了显著性成果。

ADaPT：根据需要的分解和语言模型规划

ADaPT: As-Needed Decomposition and Planning with Language Models

Large language models (LLMs) have been successfully adapted for interactive
decision-making tasks like web navigation. While achieving decent performance,
previous methods implicitly assume a forward-only execution mode for the model,
where they only provide oracle trajectories as in-context examples to teach the
model how to reason in the interactive environment. Consequently, the model
could not handle more challenging scenarios not covered in the in-context
examples, e.g., mistakes, leading to sub-optimal performance. To address this
issue, we propose to model the interactive task as state space exploration,
where the LLM agent transitions among a pre-defined set of states by performing
actions to complete the task. This formulation enables flexible back-tracking,
allowing the model to easily recover from errors. We evaluate our proposed LLM
Agent with State-Space ExploRation (LASER) on the WebShop task. Experimental
results show that our LASER agent significantly outperforms previous methods
and closes the gap with human performance on the web navigation task.

大语言模型在网页导航等交互决策任务中已成功应用，我们提出了一种基于状态空间探索的交互式任务建模方法，通过动作与状态转换来完成任务，实现了灵活的回溯以提高性能。实验结果表明，我们的 LASER 代理在网页导航任务中明显优于以往的方法，并且与人类表现之间的差距缩小。