TL-Training: A Task-Feature-Based Framework for Training Large Language Models in Tool Use

• URL: http://arxiv.org/abs/2412.15495v1
• Date: Fri, 20 Dec 2024 02:21:36 GMT
• Title: TL-Training: A Task-Feature-Based Framework for Training Large Language Models in Tool Use
• Authors: Junjie Ye, Yilong Wu, Sixian Li, Yuming Yang, Tao Gui, Qi Zhang, Xuanjing Huang, Peng Wang, Zhongchao Shi, Jianping Fan, Zhengyin Du,
• Abstract summary: Large language models (LLMs) achieve remarkable advancements by leveraging tools to interact with external environments. Standard supervised fine-tuning approach, which relies on large-scale datasets, often overlooks task-specific characteristics in tool use. We propose TL-Training, a task-feature-based framework that mitigates the effects of suboptimal training data.
• Score: 46.20445033086643
• License:
• Abstract: Large language models (LLMs) achieve remarkable advancements by leveraging tools to interact with external environments, a critical step toward generalized AI. However, the standard supervised fine-tuning (SFT) approach, which relies on large-scale datasets, often overlooks task-specific characteristics in tool use, leading to performance bottlenecks. To address this issue, we analyze three existing LLMs and uncover key insights: training data can inadvertently impede tool-use behavior, token importance is distributed unevenly, and errors in tool calls fall into a small set of distinct categories. Building on these findings, we propose TL-Training, a task-feature-based framework that mitigates the effects of suboptimal training data, dynamically adjusts token weights to prioritize key tokens during SFT, and incorporates a robust reward mechanism tailored to error categories, optimized through proximal policy optimization. We validate TL-Training by training CodeLLaMA-2-7B and evaluating it on four diverse open-source test sets. Our results demonstrate that the LLM trained by our method matches or surpasses both open- and closed-source LLMs in tool-use performance using only 1,217 training data points. Additionally, our method enhances robustness in noisy environments and improves general task performance, offering a scalable and efficient paradigm for tool-use training in LLMs. The code and data are available at https://github.com/Junjie-Ye/TL-Training.

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