用大模型自动提取领域语言语法，省去人工编写耗时
Kajal: Extracting Grammar of a Source Code Using Large Language Models
- 通过提示工程和少量样本学习，让大模型生成代码语法规则
- 少量样本下准确率达60%，无样本时为45%
- 适合需要快速构建语言解析器的开发团队
理解并提取领域特定语言（DSL）的语法对多种软件工程任务至关重要；然而，手动创建这些语法既耗时又易出错。本文提出Kajal，一种利用大语言模型（LLMs）通过提示工程和少样本学习，从DSL代码片段中自动推断语法的新方法。Kajal动态构建输入提示，利用上下文信息引导LLM生成对应语法，并通过反馈驱动的方式迭代优化。实验表明，采用少样本学习时，Kajal准确率达60%；未使用少样本时，准确率为45%，凸显了少样本学习对工具有效性的重要影响。该方法为自动化DSL语法提取提供了可行方案，未来工作将探索使用更小的开源LLM及在更大数据集上的测试，以进一步验证Kajal的性能。
原文摘要 · Abstract (English)
Understanding and extracting the grammar of a domain-specific language (DSL) is crucial for various software engineering tasks; however, manually creating these grammars is time-intensive and error-prone. This paper presents Kajal, a novel approach that automatically infers grammar from DSL code snippets by leveraging Large Language Models (LLMs) through prompt engineering and few-shot learning. Kajal dynamically constructs input prompts, using contextual information to guide the LLM in generating the corresponding grammars, which are iteratively refined through a feedback-driven approach. Our experiments show that Kajal achieves 60% accuracy with few-shot learning and 45% without it, demonstrating the significant impact of few-shot learning on the tool's effectiveness. This approach offers a promising solution for automating DSL grammar extraction, and future work will explore using smaller, open-source LLMs and testing on larger datasets to further validate Kajal's performance.
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