Making Language Models Better Tool Learners with Execution Feedback
This addresses a key challenge in tool learning for AI systems, enabling more efficient and error-free interactions with real-world tools, though it is incremental as it builds on existing methodologies.
The paper tackles the problem of large language models using tools indiscriminately for tasks they can handle themselves, which propagates errors, by proposing TRICE, a framework that uses execution feedback to teach models when and how to use tools, resulting in improved accuracy of tool usage and reduced excessive reliance.
Tools serve as pivotal interfaces that enable humans to understand and reshape the environment. With the advent of foundation models, AI systems can utilize tools to expand their capabilities and interact with the real world. Existing tool learning methodologies, encompassing supervised fine-tuning and prompt engineering approaches, often induce large language models to utilize tools indiscriminately, as complex tasks often exceed their own competencies. However, introducing tools for simple tasks, which the models themselves can readily resolve, can inadvertently propagate errors rather than enhance performance. This leads to the research question: can we teach language models when and how to use tools? To meet this need, we propose Tool leaRning wIth exeCution fEedback (TRICE), a two-stage end-to-end framework that enables the model to continually learn through feedback derived from tool execution, thereby learning when and how to use tools effectively. Experimental results, backed by further analysis, show that TRICE can make the large language model selectively use tools by improving the accuracy of tool usage while enhancing insufficient tool learning and mitigating excessive reliance on tools. Code is available at https://github.com/zjunlp/TRICE.