ADaPT: As-Needed Decomposition and Planning with Language Models
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.
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Archiki Prasad
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2023-11-13T10:46:37.552Z
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Archiki Prasad
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archiki
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Alexander Koller
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Mareike Hartmann
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2023-11-13T10:46:52.405Z
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Mareike Hartmann
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mahartmann
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Peter Clark
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2023-11-14T09:38:01.525Z
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Peter Clark
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pclark425
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Ashish Sabharwal
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2025-07-22T08:01:54.359Z
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Ashish Sabharwal
user
ashish333
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Mohit Bansal
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Tushar Khot
admin_assigned
2023-11-13T10:47:44.526Z
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Tushar Khot
user
tusharkhot
ADaPT: As-Needed Decomposition and Planning with Language Models
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.
ADaPT, a new approach for complex tasks, uses as-needed decomposition and planning, outperforming baselines in various interactive decision-making tasks.
Large Language Models iterative executors plan-and-execute sub-tasks task complexity multilevel decomposition dynamic adjustment