ProTIP: Progressive Tool Retrieval Improves Planning
Large language models (LLMs) are increasingly employed for complex multi-step
planning tasks, where the tool retrieval (TR) step is crucial for achieving
successful outcomes. Two prevalent approaches for TR are single-step retrieval,
which utilizes the complete query, and sequential retrieval using task
decomposition (TD), where a full query is segmented into discrete atomic
subtasks. While single-step retrieval lacks the flexibility to handle
"inter-tool dependency," the TD approach necessitates maintaining "subtask-tool
atomicity alignment," as the toolbox can evolve dynamically. To address these
limitations, we introduce the Progressive Tool retrieval to Improve Planning
(ProTIP) framework. ProTIP is a lightweight, contrastive learning-based
framework that implicitly performs TD without the explicit requirement of
subtask labels, while simultaneously maintaining subtask-tool atomicity. On the
ToolBench dataset, ProTIP outperforms the ChatGPT task decomposition-based
approach by a remarkable margin, achieving a 24% improvement in Recall@K=10 for
TR and a 41% enhancement in tool accuracy for plan generation.
65812aef560c1557cfd8d74c
Raviteja Anantha
65812aef560c1557cfd8d74d
Bortik Bandyopadhyay
65812aef560c1557cfd8d74e
Anirudh Kashi
65812aef560c1557cfd8d74f
Sayantan Mahinder
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2023-12-19T10:19:41.818Z
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Sayantan Mahinder
user
SayantanM
65812aef560c1557cfd8d750
Andrew W Hill
65812aef560c1557cfd8d751
Srinivas Chappidi
ProTIP: Progressive Tool Retrieval Improves Planning
Large language models (LLMs) are increasingly employed for complex multi-step
planning tasks, where the tool retrieval (TR) step is crucial for achieving
successful outcomes. Two prevalent approaches for TR are single-step retrieval,
which utilizes the complete query, and sequential retrieval using task
decomposition (TD), where a full query is segmented into discrete atomic
subtasks. While single-step retrieval lacks the flexibility to handle
"inter-tool dependency," the TD approach necessitates maintaining "subtask-tool
atomicity alignment," as the toolbox can evolve dynamically. To address these
limitations, we introduce the Progressive Tool retrieval to Improve Planning
(ProTIP) framework. ProTIP is a lightweight, contrastive learning-based
framework that implicitly performs TD without the explicit requirement of
subtask labels, while simultaneously maintaining subtask-tool atomicity. On the
ToolBench dataset, ProTIP outperforms the ChatGPT task decomposition-based
approach by a remarkable margin, achieving a 24% improvement in Recall@K=10 for
TR and a 41% enhancement in tool accuracy for plan generation.
A new contrastive learning-based framework, ProTIP, improves tool retrieval and plan generation in complex planning tasks by implicitly handling task decomposition without explicit subtask labels.
contrastive learning tool retrieval task decomposition subtask-tool atomicity ProTIP ToolBench dataset ChatGPT