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English(EN) Text2DSL: LLM-Based Code Generation for Domain-Specific Languages

LLM根据自然语言提示生成领域特定语言代码

研究人员推出Text2DSL,一种从自然语言描述生成领域特定语言(DSL)代码的方法。他们开发了PolkitBench数据集,包含超过4000个自然语言到Polkit规则的配对,并通过基于AST的管道进行了验证。使用GigaChat-10B和Nemotron-3-Nano模型的实验表明,提供结构化上下文(如BNF语法和API规范)可显著提高代码生成质量,将语法有效性提高到近99%,并将CodeBLEU分数提高高达95%。这种方法无需模型微调即可实现高质量的DSL代码生成。 AI

影响 通过实现从自然语言生成代码,增强了领域特定语言的可用性,可能降低了复杂策略管理的入门门槛。

排序理由 该集群包含一篇详细介绍新方法和代码生成数据集的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

LLM根据自然语言提示生成领域特定语言代码

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该集群包含一篇详细介绍新方法和代码生成数据集的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Shamil G. Magomedov ·

    Text2DSL:基于LLM的领域特定语言代码生成

    Domain-specific languages (DSLs) are widely used for managing operating system security policies, yet manually authoring rules in such languages demands high expertise and is error-prone. This paper formalises the task of automatic DSL code generation from natural language descri…