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Planning and Editing What You Retrieve for Enhanced Tool Learning (2404.00450v2)

Published 30 Mar 2024 in cs.CL, cs.AI, cs.IR, and cs.LG
Planning and Editing What You Retrieve for Enhanced Tool Learning

Abstract: Recent advancements in integrating external tools with 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 PLUTO (Planning, Learning, and Understanding for TOols) 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 planner that decomposes complex queries into actionable tasks, enhancing the effectiveness of tool utilization. The E&G paradigm utilizes LLMs to enrich tool descriptions based on user scenarios, bridging the gap between user queries and tool functionalities. Experiment results demonstrate that these paradigms significantly improve the recall and NDCG in tool retrieval tasks, significantly surpassing current state-of-the-art models.

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Enhanced Tool Learning in LLMs Through Planning and Editing

Introduction to PLUTo

The integration of external tools with LLMs extends the functionality of AI applications into new domains like mathematical reasoning and smart assistants. Traditional methods rely on one-time retrieval strategies that often fail to consider the dynamism of real-world queries, resulting in a gap between the user's needs and the tools retrieved. To bridge this, the paper introduces a novel framework, PLUTo (Planning, Learning, and Understanding for Tools), incorporating two paradigms: Plan-and-Retrieve (P&R) and Edit-and-Ground (E&G). These paradigms collectively aim to enhance the retrieval and utility of tools in responding to complex user queries.

Plan-and-Retrieve (P&R) Paradigm

The P&R paradigm is a neural-network-based approach that employs a query planner and a retrieval module. This paradigm operates in three stages:

  1. Decomposition: The query planner decomposes complex user queries into more manageable sub-queries.
  2. Retrieval: For each sub-query, the retriever module shortlists relevant tools from a pool of candidates.
  3. Evaluation: The effectiveness of selected tools is continuously evaluated, adjusting the planning strategy to enhance retrieval accuracy.

Edit-and-Ground (E&G) Paradigm

The E&G paradigm improves tool descriptions to better match their functionalities with user scenarios. It utilizes user query context and LLMs' world knowledge to optimize tool descriptions, making them more informative and aligned with real-world applications. This process involves:

  1. Evaluation of Existing Descriptions: Identifying under-informative tool descriptions based on retrieval performance.
  2. Optimization: Leveraging LLM capabilities to generate enriched tool descriptions that detail functionalities in relation to user scenarios.

Key Results

The implementation of the PLUTo approach yielded significant improvements in tool retrieval tasks, outperforming current state-of-the-art models. Experiments demonstrated heightened recall and normalized discounted cumulative gain (NDCG), indicating a more effective and accurate tool retrieval process. Furthermore, downstream evaluation suggested improvements in response accuracy and relevance, highlighting PLUTo's ability to address complex queries successfully.

Practical and Theoretical Implications

PLUTo offers several advancements in the field of LLMs and tool integration, including:

  • Demonstrating the efficacy of planning and editing paradigms in enhancing tool retrieval.
  • Showcasing the flexibility of PLUTo in adapting to different retrieval engines.
  • Highlighting the potential of LLMs in automating and enriching tool descriptions based on real-world user scenarios.

Future Perspectives

While PLUTo marks a significant step forward, future research may focus on several areas:

  • Extending the PLUTo framework to multilingual settings to broaden its applicability.
  • Exploring further optimization techniques within the E&G paradigm to enhance tool descriptions continually.
  • Investigating the integration of PLUTo in more specialized domains such as healthcare or legal services, potentially unlocking new uses for LLM-enhanced tool learning.

Conclusion

The research introduces and validates PLUTo, a novel framework that significantly enhances tool learning in LLMs. By integrating the P&R and E&G paradigms, PLUTo not only improves the retrieval of relevant tools but also ensures that the tools' descriptions are optimized for practical applications. As a result, this framework stands as a promising advancement in the integration of LLMs with external tools, offering improved effectiveness and adaptability across various applications.

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Authors (3)
  1. Tenghao Huang (13 papers)
  2. Dongwon Jung (3 papers)
  3. Muhao Chen (159 papers)
Citations (5)
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