Small LLMs Are Weak Tool Learners: A Multi-LLM Agent

Large Language Model (LLM) agents significantly extend the capabilities of standalone LLMs, empowering them to interact with external tools (e.g., APIs, functions) and complete various tasks in a self-directed fashion. The challenge of tool use demands that LLMs not only understand user queries and generate answers accurately but also excel in task planning, tool invocation, and result summarization. While traditional works focus on training a single LLM with all these capabilities, performance limitations become apparent, particularly with smaller models. To overcome these challenges, we propose a novel approach that decomposes the aforementioned capabilities into a planner, caller, and summarizer. Each component is implemented by a single LLM that focuses on a specific capability and collaborates with others to accomplish the task. This modular framework facilitates individual updates and the potential use of smaller LLMs for building each capability. To effectively train this framework, we introduce a two-stage training paradigm. First, we fine-tune a backbone LLM on the entire dataset without discriminating sub-tasks, providing the model with a comprehensive understanding of the task. Second, the fine-tuned LLM is used to instantiate the planner, caller, and summarizer respectively, which are continually fine-tuned on respective sub-tasks.

In addition to exploring proprietary LLMs like GPT-4, researchers have also actively engaged in developing customizable agent systems by finetuning open-source LLMs on diverse tool-use datasets (Patil et al., 2023; Tang et al., 2023; Qin et al., 2023b; Gou et al., 2023). The challenge of tool learning demands sufficiently large and complex LLMs. These models must not only comprehend user queries but also excel in task planning, tool selection and invocation, and result summarization (Yujia et al., 2023). These capabilities draw upon different facets of the LLMs; for instance, planning relies more on reasoning ability, while tool selection and invocation demand legal and accurate request writing, and result summarization requires adept conclusion-drawing skills.