ProTIP: Progressive Tool Retrieval Improves Planning
AuthorsRaviteja Anantha Ramesh, Bortik Bandyopadhyay, Anirudh Kashi, Sayantan Mahinder, Andrew W Hill, Srinivas (Vasu) Chappidi
AuthorsRaviteja Anantha Ramesh, Bortik Bandyopadhyay, Anirudh Kashi, Sayantan Mahinder, Andrew W Hill, Srinivas (Vasu) 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 necessitate 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.
August 12, 2025research area Knowledge Bases and Search, research area Speech and Natural Language Processingconference ACL
Recent advancements in long-context language models (LCLMs) have the potential to transform Retrieval-Augmented Generation (RAG) by simplifying pipelines. With their extended context windows, LCLMs can process entire knowledge bases and directly handle retrieval and reasoning. This capability is defined as In-Context Retrieval and Reasoning (ICR2). However, existing benchmarks like LOFT often overestimate LCLM performance because they lack...
December 18, 2023research area Knowledge Bases and Search, research area Speech and Natural Language ProcessingWorkshop at EACL
This paper was accepted at the UncertaiNLP workshop at EACL 2024.
Large language models (LLMs) have the remarkable ability to solve new tasks with just a few examples, but they need access to the right tools. Retrieval Augmented Generation (RAG) addresses this problem by retrieving a list of relevant tools for a given task. However, RAG's tool retrieval step requires all the required information to be explicitly present in the query. This is a...