ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings (2305.11554v4)

Abstract: Augmenting LLMs with external tools has emerged as a promising approach to solving complex problems. However, traditional methods, which finetune LLMs with tool demonstration data, can be both costly and restricted to a predefined set of tools. Recent in-context learning paradigm alleviates these issues, but the limited context length only allows for a few shots of demonstrations, leading to suboptimal understandings of the tools. Moreover, when there are numerous tools to choose from, in-context learning could completely fail to work. In this paper, we propose an alternative approach, $\textbf{ToolkenGPT}$, which combines the benefits of both sides. Our approach represents each $\underline{tool}$ as a to$\underline{ken}$ ($\textit{toolken}$) and learns an embedding for it, enabling tool calls in the same way as generating a regular word token. Once a toolken is triggered, the LLM is prompted to complete arguments for the tool to execute. ToolkenGPT offers the flexibility to plug in an arbitrary number of tools by expanding the set of toolkens on the fly. In addition, it improves tool use by allowing extensive demonstration data for learning the toolken embeddings. In diverse domains, including numerical reasoning, knowledge-based question answering, and embodied plan generation, our approach effectively augments LLMs with tools and substantially outperforms various latest baselines. ToolkenGPT demonstrates the promising ability to use relevant tools from a large tool set in complex scenarios.

ToolkenGPT: Augmenting Frozen LLMs with Massive Tools via Tool Embeddings

The paper introduces an innovative approach entitled "ToolkenGPT" designed to improve the capability of LLMs by augmenting them with external tools. This approach is aimed at addressing the limitations of traditional methods that rely on fine-tuning LLMs with tool demonstration data, which can become both resource-intensive and restricted to a limited set of tools. ToolkenGPT proposes the creation of 'toolkens'—a novel mechanism for integrating tools with LLMs by embedding each tool as a token and enabling their invocation akin to generating a word token.

The significance of ToolkenGPT lies within its ability to link LLMs with an arbitrary number of external tools via toolken embeddings. This flexible methodology enhances the LLM's utility, enabling more sophisticated problem-solving across various domains, including mathematical reasoning, knowledge-based question answering, and embodied plan generation. This capability is particularly crucial given the increasing complexity of real-world applications and the proliferation of tools such as advanced APIs and domain-specific utilities.

Key Contributions

The paper delineates the advantages of ToolkenGPT by highlighting its main contributions:

• Tool Integration without Fine-tuning: ToolkenGPT allows LLMs to access and utilize a wide array of external tools without extensive fine-tuning of the LLMs themselves. This is accomplished by training lightweight toolken embeddings, thus reducing the computational overhead typically associated with fine-tuning LLMs.
• Scalability and Adaptability: The approach supports an extensible set of tools by simply expanding the toolken embeddings, allowing LLMs to adapt quickly to new tools. This contrasts with approaches limited by the context length constraints of in-context learning that can impede the efficient use of numerous tools.
• Efficacious Application in Varied Domains: The research demonstrates that ToolkenGPT outperforms several advanced prompting techniques and baseline models in various domains, including numerical reasoning and knowledge-based question answering. The toolkens enable a more effective and contextually aware use of tools, greatly improving task performance.

Numerical Results and Comparative Analysis

The paper provides a thorough empirical validation of ToolkenGPT across several benchmarks where it showcases superior performance:

• In numerical reasoning tasks, ToolkenGPT demonstrated enhanced accuracy by efficiently utilizing mathematical operators. The results revealed the limitations of ReAct, a prominent in-context learning framework, when dealing with a multitude of tools.
• For knowledge-based question answering using KAMEL, ToolkenGPT achieved a higher accuracy rate than state-of-the-art in-context learning techniques, showcasing its strength in processing and retrieving factual data from a wide range of knowledge bases.

Practical and Theoretical Implications

Practically, ToolkenGPT offers a scalable solution for rapidly evolving environments where tools are continuously updated or newly introduced. The flexibility and reduced computational costs make it suitable for practical deployment and alignment with existing LLM infrastructure without exhaustive resource demands.

Theoretically, ToolkenGPT opens avenues for further exploration in AI, allowing for enhanced interaction between natural language processing and action-oriented tasks. Its novel approach to tool integration via embeddings can prompt the rethinking of how external utilities are interfaced with LLMs, potentially fostering advancements in context-aware computational reasoning and dynamic problem-solving.

Speculation on Future Developments

Looking forward, ToolkenGPT could pave the way for more profound integrations of AI models in applications necessitating real-time tool interaction. The concept of toolkens could be extended to support the development of autonomous agents capable of interfacing seamlessly with a myriad of software tools, thereby bridging the gap between human-level intelligence emulation and practical tool usage efficiency.

Overall, this paper makes a substantial contribution to AI research by showcasing an efficient and scalable approach to improve LLMs' functionality through toolkens. This paradigm not only optimizes embedded machine intelligence within practical boundaries but also inspires further research into adaptable, efficient LLM integration solutions for complex, multi-tool scenarios.