ProTIP: Progressive Tool Retrieval Improves Planning
Raviteja Anantha Bortik Bandyopadhyay Anirudh Kashi Sayantan Mahinder Andrew W Hill Srinivas Chappidi
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.