تقرير
ProTIP: Progressive Tool Retrieval Improves Planning
| العنوان: | ProTIP: Progressive Tool Retrieval Improves Planning |
|---|---|
| المؤلفون: | Anantha, Raviteja, Bandyopadhyay, Bortik, Kashi, Anirudh, Mahinder, Sayantan, Hill, Andrew W, Chappidi, Srinivas |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Information Retrieval, Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
| الوصف: | 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. Comment: preprint version |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2312.10332 |
| رقم الأكسشن: | edsarx.2312.10332 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |