Summary of Planning and Editing What You Retrieve For Enhanced Tool Learning, by Tenghao Huang et al.
Planning and Editing What You Retrieve for Enhanced Tool Learning
by Tenghao Huang, Dongwon Jung, Muhao Chen
First submitted to arxiv on: 30 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Recent advancements in integrating external tools with Large Language Models (LLMs) have led to innovative applications in mathematical reasoning, code generators, and smart assistants. However, existing methods rely on simple one-time retrieval strategies, which fall short on effectively and accurately shortlisting relevant tools. This paper proposes a novel PLUTO approach, comprising Plan-and-Retrieve (P&R) and Edit-and-Ground (E&G) paradigms. The P&R paradigm combines a neural retrieval module for tool shortlisting and an LLM-based query planner that decomposes complex queries into actionable tasks, enhancing tool utilization effectiveness. The E&G paradigm leverages LLMs to enrich tool descriptions based on user scenarios, bridging the gap between user queries and tool functionalities. Experimental results demonstrate that these paradigms significantly improve recall and NDCG in tool retrieval tasks, surpassing current state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how we can use computers to help us find the right tools for different jobs. Right now, it’s hard to find the best tool for a specific task because our computer systems don’t understand what we need. The researchers created a new way to search for tools that uses two different methods: one that plans ahead and another that refines its search based on user needs. This new approach was tested and showed great improvements in finding the right tools compared to current methods. |
Keywords
* Artificial intelligence * Recall
