Planning and Editing What You Retrieve for Enhanced Tool Learning

arXiv:2404.00450v1 Announce Type: new
Abstract: Recent advancements in integrating external tools with Large Language Models (LLMs) have opened new frontiers, with applications in mathematical reasoning, code generators, and smart assistants. However, existing methods, relying on simple one-time retrieval strategies, fall short on effectively and accurately shortlisting relevant tools. This paper introduces a novel \modelname (\modelmeaning) approach, encompassing ``Plan-and-Retrieve (P\&R)'' and ``Edit-and-Ground (E\&G)'' paradigms. The P\&R paradigm consists of a neural retrieval module for shortlisting relevant tools and an LLM-based query …

abstract applications arxiv assistants code cs.cl editing frontiers generators however language language models large language large language models llms mathematical reasoning paper planning reasoning retrieval simple smart strategies tool tools type