解决阿拉伯语函数调用的结构不稳定问题，提升代码生成准确率。
From Language to Action in Arabic: Reliable Structured Tool Calling via Data-Centric Fine-Tuning
- 通过数据审计与提示重构，对270M参数模型进行全参数微调。
- 解析失败率从87%降至1%以下，函数名准确率提升八倍以上。
- 适合需要高可靠性的阿拉伯语智能体系统开发者使用。
函数调用语言模型是将自然语言转化为可执行结构化动作的关键组件，但现有模型在处理阿拉伯语时存在严重结构不稳定性。本文提出AISA-AR-FunctionCall，一个基于270M参数FunctionGemma模型的生产级阿拉伯语函数调用框架，通过系统性数据审计、模式修复、工具感知提示重构及全参数监督微调实现优化。在预留测试集上，微调使解析失败率从87%降至1%以下，函数名准确率提升超过八倍，并显著改善跨方言与跨领域的参数对齐。错误分析表明，问题由结构崩溃转向语义错位，说明序列化稳定性和决策推理是可分离挑战。进一步探索了引入显式中间推理的LoRA增强版本，提升调用前推理能力。所有数据集与模型均在AISA框架下开源。
原文摘要 · Abstract (English)
Function-calling language models are essential for agentic AI systems that translate natural language into executable structured actions, yet existing models exhibit severe structural instability when applied to Arabic. We present AISA-AR-FunctionCall, a production-oriented Arabic function-calling framework built on a 270M-parameter FunctionGemma backbone and trained through systematic dataset auditing, schema repair, tool-aware prompt restructuring, and full-parameter supervised fine-tuning. On a held-out test set, fine-tuning reduces parse failures from 87\% to below 1\%, improves function name accuracy by more than eightfold, and substantially enhances argument alignment across dialects and domains. Error analysis reveals a transition from structural collapse to semantic misalignment, suggesting that serialization stability and decision-level reasoning are separable challenges. We further explore a reasoning-augmented LoRA variant that introduces explicit intermediate reasoning prior to tool invocation. All datasets and models are publicly released under the AISA framework.
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