Re-Invoke: Tool Invocation Rewriting for Zero-Shot Tool Retrieval

• URL: http://arxiv.org/abs/2408.01875v1
• Date: Sat, 3 Aug 2024 22:49:27 GMT
• Title: Re-Invoke: Tool Invocation Rewriting for Zero-Shot Tool Retrieval
• Authors: Yanfei Chen, Jinsung Yoon, Devendra Singh Sachan, Qingze Wang, Vincent Cohen-Addad, Mohammadhossein Bateni, Chen-Yu Lee, Tomas Pfister,
• Abstract summary: Re-Invoke is an unsupervised tool retrieval method designed to scale effectively to large toolsets without training. We employ a novel multi-view similarity ranking strategy based on intents to pinpoint the most relevant tools for each query. Our evaluation demonstrates that Re-Invoke significantly outperforms state-of-the-art alternatives in both single-tool and multi-tool scenarios.
• Score: 47.81307125613145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in large language models (LLMs) have enabled autonomous agents with complex reasoning and task-fulfillment capabilities using a wide range of tools. However, effectively identifying the most relevant tools for a given task becomes a key bottleneck as the toolset size grows, hindering reliable tool utilization. To address this, we introduce Re-Invoke, an unsupervised tool retrieval method designed to scale effectively to large toolsets without training. Specifically, we first generate a diverse set of synthetic queries that comprehensively cover different aspects of the query space associated with each tool document during the tool indexing phase. Second, we leverage LLM's query understanding capabilities to extract key tool-related context and underlying intents from user queries during the inference phase. Finally, we employ a novel multi-view similarity ranking strategy based on intents to pinpoint the most relevant tools for each query. Our evaluation demonstrates that Re-Invoke significantly outperforms state-of-the-art alternatives in both single-tool and multi-tool scenarios, all within a fully unsupervised setting. Notably, on the ToolE datasets, we achieve a 20% relative improvement in nDCG@5 for single-tool retrieval and a 39% improvement for multi-tool retrieval.

Related papers

• Toolshed: Scale Tool-Equipped Agents with Advanced RAG-Tool Fusion and Tool Knowledge Bases [0.0]
  We introduce Toolshed Knowledge Bases, a tool knowledge base (vector database) designed to store enhanced tool representations. We also propose Advanced RAG-Tool Fusion, a novel ensemble of tool-applied advanced retrieval-augmented generation (RAG) techniques. Our approach achieves 46%, 56%, and absolute improvements on the ToolE single-tool, ToolE multi-tool and Seal-Tools benchmark datasets.
  arXiv  Detail & Related papers  (2024-10-18T16:44:22Z)
• ToolGen: Unified Tool Retrieval and Calling via Generation [34.34787641393914]
  We introduce ToolGen, a paradigm shift that integrates tool knowledge directly into the large language models' parameters. We show that ToolGen achieves superior results in both tool retrieval and autonomous task completion. ToolGen paves the way for more versatile, efficient, and autonomous AI systems.
  arXiv  Detail & Related papers  (2024-10-04T13:52:32Z)
• Efficient and Scalable Estimation of Tool Representations in Vector Space [34.767193045989515]
  We present a framework for generating synthetic data for tool retrieval applications and an efficient data-driven tool retrieval strategy using small encoder models. We create ToolBank, a new tool retrieval dataset that reflects real human user usages. With these new methods, we achieve improvements of up to 27.28 in Recall@K on the ToolBench dataset and 30.5 in Recall@K on ToolBank.
  arXiv  Detail & Related papers  (2024-09-02T19:39:24Z)
• Enhancing Tool Retrieval with Iterative Feedback from Large Language Models [9.588592185027455]
  Large language models (LLMs) can effectively handle a certain amount of tools through in-context learning or fine-tuning. In real-world scenarios, the number of tools is typically extensive and irregularly updated, emphasizing the necessity for a dedicated tool retrieval component. We propose to enhance tool retrieval with iterative feedback from the large language model.
  arXiv  Detail & Related papers  (2024-06-25T11:12:01Z)
• Chain of Tools: Large Language Model is an Automatic Multi-tool Learner [54.992464510992605]
  Automatic Tool Chain (ATC) is a framework that enables the large language models (LLMs) to act as a multi-tool user. To scale up the scope of the tools, we next propose a black-box probing method. For a comprehensive evaluation, we build a challenging benchmark named ToolFlow.
  arXiv  Detail & Related papers  (2024-05-26T11:40:58Z)
• Towards Completeness-Oriented Tool Retrieval for Large Language Models [60.733557487886635]
  Real-world systems often incorporate a wide array of tools, making it impractical to input all tools into Large Language Models. Existing tool retrieval methods primarily focus on semantic matching between user queries and tool descriptions. We propose a novel modelagnostic COllaborative Learning-based Tool Retrieval approach, COLT, which captures not only the semantic similarities between user queries and tool descriptions but also takes into account the collaborative information of tools.
  arXiv  Detail & Related papers  (2024-05-25T06:41:23Z)
• Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios [93.68764280953624]
  UltraTool is a novel benchmark designed to improve and evaluate Large Language Models' ability in tool utilization. It emphasizes real-world complexities, demanding accurate, multi-step planning for effective problem-solving. A key feature of UltraTool is its independent evaluation of planning with natural language, which happens before tool usage.
  arXiv  Detail & Related papers  (2024-01-30T16:52:56Z)
• EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction [56.02100384015907]
  EasyTool is a framework transforming diverse and lengthy tool documentation into a unified and concise tool instruction. It can significantly reduce token consumption and improve the performance of tool utilization in real-world scenarios.
  arXiv  Detail & Related papers  (2024-01-11T15:45:11Z)
• MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use [82.24774504584066]
  Large language models (LLMs) have garnered significant attention due to their impressive natural language processing (NLP) capabilities. We introduce MetaTool, a benchmark designed to evaluate whether LLMs have tool usage awareness and can correctly choose tools. We conduct experiments involving eight popular LLMs and find that the majority of them still struggle to effectively select tools.
  arXiv  Detail & Related papers  (2023-10-04T19:39:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.