Re-Invoke: Tool Invocation Rewriting for Zero-Shot Tool Retrieval
Re-Invoke achieves a 20% nDCG@5 improvement in single-tool retrieval and 39% in multi-tool on ToolE dataset.
Key Findings
Methodology
Re-Invoke employs synthetic query generation to enrich tool documents and extracts key intents from user queries during inference. It uses a multi-view similarity ranking strategy to identify the most relevant tools. This method scales to large toolsets without training.
Key Results
- On the ToolE dataset, Re-Invoke improves single-tool retrieval nDCG@5 by 20% and multi-tool retrieval by 39%.
- Re-Invoke outperforms state-of-the-art alternatives in unsupervised settings.
- Significant accuracy improvement through intent extraction and document expansion.
Significance
Re-Invoke addresses the bottleneck of identifying relevant tools as toolset size grows, enhancing tool utilization reliability. Its unsupervised nature makes it suitable for rapidly evolving tool environments, impacting both academia and industry.
Technical Contribution
Re-Invoke's technical contribution lies in its unsupervised tool retrieval method, leveraging LLMs for document expansion and intent extraction, eliminating the need for labeled data, and providing a scalable solution.
Novelty
Re-Invoke is the first to achieve effective retrieval in large toolsets under unsupervised settings, innovatively combining synthetic query generation and multi-view similarity ranking.
Limitations
- In handling extremely complex user queries, intent recognition errors may occur, affecting retrieval results.
- The method heavily relies on the quality of generated synthetic queries, which may impact final retrieval effectiveness.
Future Work
Future work could explore improving the quality of synthetic queries and testing Re-Invoke's performance on larger and more diverse toolsets.
AI Executive Summary
Recent advances in large language models (LLMs) have enabled autonomous agents to perform complex reasoning and task fulfillment using various tools. However, as the toolset size grows, effectively identifying the most relevant tools becomes a key bottleneck. To address this, the paper introduces Re-Invoke, an unsupervised tool retrieval method designed to scale effectively to large toolsets without training.
The core of Re-Invoke lies in generating diverse synthetic queries to comprehensively cover the query space associated with each tool document. During inference, it leverages LLM's query understanding capabilities to extract key tool-related context and underlying intents from user queries. Finally, it employs a novel multi-view similarity ranking strategy based on intents to pinpoint the most relevant tools for each query.
Experimental results demonstrate that Re-Invoke significantly outperforms state-of-the-art alternatives in both single-tool and multi-tool scenarios, notably achieving a 20% relative improvement in nDCG@5 for single-tool retrieval and a 39% improvement for multi-tool retrieval on the ToolE datasets. This method not only holds significant academic value but also offers new possibilities for industrial applications. Future research could focus on further improving the quality of synthetic queries and testing its performance on even larger toolsets.
Deep Analysis
Background
In recent years, large language models (LLMs) have shown impressive capabilities in complex tasks, including math, reasoning, and coding. However, LLMs are pre-trained on static corpora, limiting their adaptability to the rapidly evolving real world. Augmenting LLMs with external tools, rather than relying solely on their internal knowledge, could unlock their potential to tackle even more challenging problems.
Core Problem
As the toolset size grows, effectively identifying the most relevant tools becomes a key bottleneck. This problem is crucial because selecting the wrong tools can lead to task failure or inefficiency, and the challenge lies in the diversity and complexity of user queries.
Innovation
Re-Invoke's core innovations include: 1) generating diverse synthetic queries to enrich tool documents, 2) extracting key intents from user queries, 3) employing a multi-view similarity ranking strategy. These innovations enable Re-Invoke to achieve efficient tool retrieval in unsupervised settings.
Methodology
- �� Synthetic query generation: Use LLMs to generate diverse synthetic queries to enrich tool documents.
- �� Intent extraction: Extract key intents from user queries during inference.
- �� Multi-view similarity ranking: Use a multi-view similarity ranking strategy based on intents to identify the most relevant tools.
Experiments
Experiments use the ToolBench and ToolE datasets to evaluate Re-Invoke's tool retrieval performance. nDCG@5 is used as the main evaluation metric, comparing performance differences with baseline methods like BM25 and HyDE.
Results
Re-Invoke improves single-tool retrieval nDCG@5 by 20% and multi-tool retrieval by 39% on the ToolE dataset. It outperforms state-of-the-art alternatives in unsupervised settings.
Applications
Re-Invoke is applicable in scenarios requiring rapid identification of relevant tools, such as intelligent assistants and automated task allocation. Its unsupervised nature makes it widely applicable in dynamically changing tool environments.
Limitations & Outlook
Re-Invoke may encounter intent recognition errors when handling complex user queries. Additionally, its reliance on the quality of synthetic queries may impact final retrieval effectiveness. Future improvements could focus on enhancing synthetic query quality and expanding testing scale.
Plain Language Accessible to non-experts
Imagine you're in a huge toolbox looking for the right tool to complete a task. Re-Invoke acts like a smart assistant that understands what you want to do and quickly finds the most suitable tool. It generates various possible task descriptions to ensure every tool in the toolbox has clear usage instructions. When you make a request, it identifies your true needs and finds the most suitable tool based on those needs. It's like shopping in a large supermarket with someone helping you find all the items you need.
ELI14 Explained like you're 14
Imagine you're playing a game and need to choose the right items to complete a mission. Re-Invoke is like the smart assistant in the game that understands your mission goals and quickly finds the most suitable items. It generates various task descriptions to ensure each item has clear usage instructions. When you make a request, it identifies your true needs and finds the most suitable items based on those needs. It's like having an assistant in the game helping you find all the items you need to complete the mission faster!
Glossary
Re-Invoke
An unsupervised tool retrieval method that identifies relevant tools through synthetic query generation and intent extraction.
Used for efficient retrieval in large toolsets.
nDCG@5
A metric for evaluating retrieval system performance, considering result relevance and ranking.
Used to assess Re-Invoke's performance in tool retrieval tasks.
LLM
Large language models capable of processing and generating natural language.
Used for generating synthetic queries and extracting user intents.
Multi-view Similarity
A strategy that evaluates similarity from multiple perspectives to improve retrieval accuracy.
Used in Re-Invoke to identify the most relevant tools.
Synthetic Query
Model-generated simulated user queries used to enrich tool documents.
Used in Re-Invoke to enhance tool retrieval coverage.
Open Questions Unanswered questions from this research
- 1 How to validate Re-Invoke's performance on larger and more diverse toolsets?
- 2 How to improve synthetic query quality to further enhance retrieval effectiveness?
Applications
Immediate Applications
Intelligent Assistants
Re-Invoke can be used in intelligent assistants to help quickly identify and invoke suitable tools, improving task completion efficiency.
Long-term Vision
Automated Task Allocation
In complex task environments, Re-Invoke can be used for automated task allocation, ensuring the most suitable tools are used to complete tasks.
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.