title: ProTIP: Progressive Tool Retrieval Improves Planning
summary: 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.
paper_authors._id: 65812aef560c1557cfd8d74c
paper_authors.name: Raviteja Anantha
paper_authors._id: 65812aef560c1557cfd8d74d
paper_authors.name: Bortik Bandyopadhyay
paper_authors._id: 65812aef560c1557cfd8d74e
paper_authors.name: Anirudh Kashi
paper_authors._id: 65812aef560c1557cfd8d74f
paper_authors.name: Sayantan Mahinder
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-19T10:19:41.818Z
paper_authors._id: 644b49eb91252984f647bbe5
paper_authors.avatarUrl: /avatars/2525d392b0293c3a55a6866f68ba3d24.svg
paper_authors.fullname: Sayantan Mahinder
paper_authors.type: user
paper_authors.user: SayantanM
paper_authors._id: 65812aef560c1557cfd8d750
paper_authors.name: Andrew W Hill
paper_authors._id: 65812aef560c1557cfd8d751
paper_authors.name: Srinivas Chappidi
paper_title: ProTIP: Progressive Tool Retrieval Improves Planning
paper_summary: 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.
paper_ai_keywords: contrastive learning task decomposition subtask labels subtask-tool atomicity ToolBench dataset ChatGPT Recall@K=10 tool accuracy plan generation