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。