ToolGen: Unified Tool Retrieval and Calling via Generation (AI summary)

Key Points

1. The paper introduces ToolGen, a novel framework that integrates tool retrieval and execution into the large language model's (LLM's) generative process using virtual tokens.

2. ToolGen enables the LLM to generate tool calls and arguments as part of its next token prediction capabilities, seamlessly blending tool invocation with language generation.

3. ToolGen's framework allows the LLM to access and utilize a vast amount of tools with no additional retrieval step, significantly enhancing both performance and scalability.

4. Experimental results with over 47,000 tools show that ToolGen not only achieves superior results in both tool retrieval and autonomous task completion, but also sets the stage for a new era of AI agents that can adapt to tools across diverse domains.

5. ToolGen fundamentally transforms tool retrieval into a generative process, paving the way for more versatile, efficient, and autonomous AI systems.

6. ToolGen enables end-to-end tool learning and opens opportunities for integration with other advanced techniques such as chain-of-thought and reinforcement learning, thereby expanding the practical capabilities of LLMs.

7. The paper presents a three-stage training process for ToolGen - tool memorization, retrieval training, and agent training - that enables efficient and scalable tool retrieval and API calling.

8. Experimental validation demonstrates that ToolGen achieves comparable performance to current best tool retrieval methods with significantly less cost and higher efficiency, and surpasses traditional tool learning paradigms across large-scale tool repositories.

9. ToolGen represents a paradigm shift in tool interaction by merging retrieval and generation into a single, cohesive model, setting the stage for a new generation of AI agents capable of adapting to a vast array of tools across diverse domains.

Summary

The research paper titled "ToolGen: Unified Tool Retrieval and Calling via Generation" introduces a novel framework, ToolGen, designed to leverage large language models (LLMs) to retrieve and utilize external tools in real-world applications. Traditional methods for tool interaction are often limited by context length and the need for separate retrieval mechanisms. ToolGen seeks to address these limitations while enhancing the performance and scalability of LLMs in tool retrieval and utilization.

ToolGen Framework
ToolGen integrates tool knowledge directly into the LLM's parameters through atomic indexing, representing each tool as a unique token. It expands the LLM's vocabulary with tool-specific virtual tokens, allowing the LLM to generate tool calls and arguments as part of its next token prediction capabilities. The training process consists of tool memorization, retrieval training, and end-to-end agent tuning to equip LLMs with knowledge of the tools, link the virtual tool token space to user queries, and fine-tune the model as an autonomous agent.

Experimental Results
Experimental results with over 47,000 real-world tools demonstrate that ToolGen achieves superior performance in both tool retrieval and autonomous task completion compared to traditional methods. ToolGen sets a new benchmark for scalable and efficient AI agents that can adapt to a wide array of tools across diverse domains. The paper highlights the potential applications of ToolGen, such as integrating advanced techniques like chain-of-thought reasoning and reinforcement learning, to further enhance the autonomy and versatility of LLMs in real-world applications. The paper provides a detailed evaluation of ToolGen's performance in tool retrieval and end-to-end agent tasks, comparing it with other baseline models and indexing methods. It demonstrates ToolGen's robustness and superior performance, highlighting its efficiency, generative capabilities, and ability to handle complex real-world retrieval tasks. The paper also discusses ablation studies and proposes a retry mechanism to mitigate the impact of early termination and sorry responses during inference. Additionally, the paper provides a detailed description of the dataset, experimental settings, and tool examples used in the study.

New Paradigm
The research paper introduces a new paradigm, ToolGen, that aims to enhance large language models' interactions with external tools. ToolGen integrates tool knowledge into the language model's parameters, enabling the model to generate tool calls and arguments as part of its prediction capabilities. This approach significantly improves performance and scalability compared to traditional methods that rely on separate tool retrieval mechanisms.

Data and Training Process
The study outlines the process of adapting and converting ToolGen data from ToolBench data, using tool documentations as data for tool memorization training and annotated queries with relevant tools for retrieval training. The research also involves end-to-end agent-tuning, where the model generates actions and inputs to complete a given task, providing the final answer. The study evaluates ToolGen's performance on unseen queries and tools and compares it with other models, demonstrating its generalization capabilities and significance in completing full tasks.

System Capabilities
Additionally, the paper presents examples of the ToolGen system's inference, dataset examples for tool memorization and retrieval training, and end-to-end agent-tuning, showcasing the model's capabilities and interactions with users. The study also includes ablation results for end-to-end evaluation, demonstrating the importance of different training stages in ToolGen’s generalization capabilities and retrieval training for unseen tools. Furthermore, the research addresses the generalization problem in generative retrieval and leaves it for future work.

Overall, the paper details the development and evaluation of ToolGen, a new approach that integrates tool knowledge into large language models, significantly improving their interaction with external tools and overall performance. The study's findings highlight ToolGen's effectiveness in generating tool calls and arguments within the language model's prediction capabilities, showcasing its potential impact on language model enhancements and interactions with external tools.

Reference: https://arxiv.org/abs/2410.03439