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LLMs generate domain-specific language code from natural language prompts

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Text2DSL, a method for generating code for domain-specific languages (DSLs) from natural language descriptions. They developed the PolkitBench dataset, containing over 4,000 natural-language-to-Polkit-rule pairs, validated through an AST-based pipeline. Experiments with GigaChat-10B and Nemotron-3-Nano models showed that providing structured context, such as BNF grammar and API specifications, significantly improves code generation quality, increasing syntactic validity to nearly 99% and CodeBLEU scores by up to 95%. This approach enables high-quality DSL code generation without requiring model fine-tuning. AI

IMPACT Enhances the usability of domain-specific languages by enabling code generation from natural language, potentially lowering the barrier to entry for complex policy management.

RANK_REASON The cluster contains an academic paper detailing a new method and dataset for code generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs generate domain-specific language code from natural language prompts

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The cluster contains an academic paper detailing a new method and dataset for code generation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

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

    Text2DSL: LLM-Based Code Generation for Domain-Specific Languages

    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…

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