通过优化语言与参数多样性，生成更高质量的函数调用训练数据。
Linguistic and Argument Diversity in Synthetic Data for Function-Calling Agents
- 用通用多样性指标自动生成请求和参数，无需人工规则
- 在保持准确率的同时，多样性显著优于现有方法
- 训练出的模型在分布外任务上性能提升7.4%
构建函数调用智能体已成为扩展模型能力的有前景方向。该任务的主要挑战在于获取高质量、多样化的训练数据。以往工作关注函数种类、调用模式和交互轮次的多样性，但对请求的语言多样性及参数覆盖（如 exttt{city_name}, exttt{stock_ticker}）仍关注不足。我们提出一种方法，通过优化查询和参数层面的通用多样性指标生成合成数据集，无需依赖手工规则或分类体系，因而具备跨场景鲁棒性。通过内在与外在测试验证，相比当前最优数据生成方法，本方法在保持相近正确率的前提下显著提升多样性。当用作训练数据时，基于该数据集训练的模型在分布外性能表现更优，在 BFCL 基准上相较同类方法准确率提升 7.4%。
原文摘要 · Abstract (English)
The construction of function calling agents has emerged as a promising avenue for extending model capabilities. A major challenge for this task is obtaining high quality diverse data for training. Prior work emphasizes diversity in functions, invocation patterns, and interaction turns, yet linguistic diversity of requests and coverage of arguments (e.g., \texttt{city\_name}, \texttt{stock\_ticker}) remain underexplored. We propose a method that generates synthetic datasets via optimizing general-purpose diversity metrics across both queries and arguments, without relying on hand-crafted rules or taxonomies, making it robust to different usecases. We demonstrate the effectiveness of our technique via both intrinsic and extrinsic testing, comparing it to SoTA data generation methods. We show a superiority over baselines in terms of diversity, while keeping comparable correctness. Additionally, when used as a training set, the model resulting from our dataset exhibits superior performance compared to analogous models based on the baseline data generation methods in out-of-distribution performance. In particular, we achieve an $7.4\%$ increase in accuracy on the BFCL benchmark compared to similar counterparts.
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