title: ADaPT: As-Needed Decomposition and Planning with Language Models
summary: 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.
paper_authors._id: 65519af50f44a05d81ef65c9
paper_authors.name: Archiki Prasad
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-13T10:46:37.552Z
paper_authors._id: 607aeae5d2cd8c150e6ae074
paper_authors.avatarUrl: /avatars/a087743b98b6fe2181283a9610db4ec4.svg
paper_authors.fullname: Archiki Prasad
paper_authors.type: user
paper_authors.user: archiki
paper_authors._id: 65519af50f44a05d81ef65ca
paper_authors.name: Alexander Koller
paper_authors._id: 65519af50f44a05d81ef65cb
paper_authors.name: Mareike Hartmann
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-13T10:46:52.405Z
paper_authors._id: 5e8341e130dc073f817a2bd4
paper_authors.avatarUrl: /avatars/3de44416dcdc67fc3da3150da33ca9b3.svg
paper_authors.fullname: Mareike Hartmann
paper_authors.type: user
paper_authors.user: mahartmann
paper_authors._id: 65519af50f44a05d81ef65cc
paper_authors.name: Peter Clark
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-11-14T09:38:01.525Z
paper_authors._id: 60ad3e1ce3de7c7440abb860
paper_authors.avatarUrl: /avatars/af0862299374f60da49f3de8de3f9ab1.svg
paper_authors.fullname: Peter Clark
paper_authors.type: user
paper_authors.user: pclark425
paper_authors._id: 65519af50f44a05d81ef65cd
paper_authors.name: Ashish Sabharwal
paper_authors._id: 65519af50f44a05d81ef65ce
paper_authors.name: Mohit Bansal
paper_authors._id: 65519af50f44a05d81ef65cf
paper_authors.name: Tushar Khot
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-13T10:47:44.526Z
paper_authors._id: 62cdf2dbaac2c91c95581830
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62cdf2dbaac2c91c95581830/N3NuO7j53qkauJOf3kkRS.jpeg
paper_authors.fullname: Tushar Khot
paper_authors.type: user
paper_authors.user: tusharkhot
paper_title: ADaPT: As-Needed Decomposition and Planning with Language Models
paper_summary: 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.
paper_ai_summary: ADaPT, a new approach for complex tasks, uses as-needed decomposition and planning, outperforming baselines in various interactive decision-making tasks.
paper_ai_keywords: Large Language Models iterative executors plan-and-execute sub-tasks task complexity multilevel decomposition dynamic adjustment