构建动态工具检索的金融对话数据生成框架，提升LLM真实场景工具使用能力。
FinToolSyn: A forward synthesis Framework for Financial Tool-Use Dialogue Data with Dynamic Tool Retrieval
- 从用户角色出发正向生成对话，模拟真实事件驱动需求
- 构建4.3万工具库，生成超14.8万含动态检索的对话实例
- 适合金融AI研发、工具调用模型训练与评估的高仿真数据需求
金融领域依赖大量投资标的和数据密集型查询，大型语言模型（LLMs）需具备工具使用能力。现有数据合成方法多采用逆向范式，从预选工具生成用户问题，导致问题过于显式，无法捕捉真实需求中隐含的、由事件驱动的特性。同时，其依赖静态工具集，忽略了在庞大工具空间中动态检索的实际过程。为此，我们提出面向金融工具使用的正向合成框架FinToolSyn，涵盖人物设定、原子工具合成到动态检索对话生成的完整流程。该框架构建了包含43,066个工具的资源库，合成超过148,000条对话实例，并引入动态检索机制以模拟真实场景下噪声候选集的复杂性。此外，我们建立了专用基准测试工具调用能力。大量实验表明，基于FinToolSyn训练的模型性能提升21.06%，为金融场景下的工具学习提供了坚实基础。
原文摘要 · Abstract (English)
Tool-use capabilities are vital for Large Language Models (LLMs) in finance, a domain characterized by massive investment targets and data-intensive inquiries. However, existing data synthesis methods typically rely on a reverse synthesis paradigm, generating user queries from pre-sampled tools. This approach inevitably introduces artificial explicitness, yielding queries that fail to capture the implicit, event-driven nature of real-world needs. Moreover, its reliance on static tool sets overlooks the dynamic retrieval process required to navigate massive tool spaces. To address these challenges, we introduce \textit{FinToolSyn}, a forward synthesis framework designed to generate high-quality financial dialogues. Progressing from persona instruction and atomic tool synthesis to dynamic retrieval dialogue generation, our pipeline constructs a repository of 43,066 tools and synthesizes over 148k dialogue instances, incorporating dynamic retrieval to emulate the noisy candidate sets typical of massive tool spaces. We also establish a dedicated benchmark to evaluate tool-calling capabilities in realistic financial scenarios. Extensive experiments demonstrate that models trained on FinToolSyn achieve a 21.06\% improvement, providing a robust foundation for tool learning in financial scenarios.
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