用试错反思机制提升大模型长上下文工具调用能力
Try, Check and Retry: A Divide-and-Conquer Framework for Boosting Long-context Tool-Calling Performance of LLMs
- 采用试-检-重试分治策略降低推理难度
- 训练自由版提升25.10%性能，训练版媲美闭源模型
- 适合需要高可靠工具调用的复杂任务场景
工具调用使大语言模型能够与外部环境交互，但现有方法在处理长上下文中的海量噪声候选工具时表现不佳，限制了实际应用。为此，我们提出Tool-DC框架，通过“试-检-重试”范式降低推理难度，并充分利用大模型的自我反思能力来提升工具调用性能。该框架包含两种变体：无需训练的Tool-DC (TF)，即插即用且灵活；基于训练的Tool-DC (TB)，推理更高效。大量实验表明，两者均显著优于基线方法。Tool-DC (TF) 在BFCL和ACEBench基准上平均性能提升最高达25.10%；Tool-DC (TB) 使Qwen2.5-7B模型在性能上达到甚至超过OpenAI o3和Claude-Haiku-4.5等专有模型水平。
原文摘要 · Abstract (English)
Tool-calling empowers Large Language Models (LLMs) to interact with external environments. However, current methods often struggle to handle massive and noisy candidate tools in long-context tool-calling tasks, limiting their real-world application. To this end, we propose Tool-DC, a Divide-and-Conquer framework for boosting tool-calling performance of LLMs. The core of Tool-DC is to reduce the reasoning difficulty and make full use of self-reflection ability of LLMs via a "Try-Check-Retry" paradigm. Specifically, Tool-DC involves two variants: 1) the training-free Tool-DC (TF), which is plug-and-play and flexible; 2) the training-based Tool-DC (TB), which is more inference-efficient. Extensive experiments show that both Tool-DC methods outperform their counterparts by a clear margin. Tool-DC (TF) brings up to +25.10% average gains against the baseline on BFCL and ACEBench benchmarks, while Tool-DC (TB) enables Qwen2.5-7B to achieve comparable or even better performance than proprietary LLMs, e.g., OpenAI o3 and Claude-Haiku-4.5.
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