Augmenting large language models (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.

本研究提出了一种名为 ToolkenGPT 的方法，通过 in-context learning 实现大量工具集的无缝扩展，从而有效提升了 LLM 在数字推理、问题回答和行动计划生成等领域中的性能表现。