Metamodel-Guided LLM Decoding for Consistent DSL Generation
We present MeMoDecode: a decoding framework that enforces a DSL's concrete syntax, abstract syntax, and well-formedness constraints jointly.
ProofPaper ↗
Key points
- Large language models (LLMs) are increasingly used to generate software engineering artifacts written in domain-specific languages (DSLs).
- Correct DSL instances usually must comply with not only the concrete syntax but also the abstract syntax defined by a metamodel augmented with semantic well-formedness constraints.
- Specifically, whenever the LLM generates a complete metamodel concept, MeMoDecode incrementally maps the current output to a partial model and verifies whether it can still be completed into a consistent instance.
- These results show that metamodel-guided decoding is an effective approach for enforcing DSL consistency during decoding across diverse DSLs and LLM scales.
Sources (1)
- [1]Metamodel-Guided LLM Decoding for Consistent DSL GenerationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 01:49 PM
We present MeMoDecode: a decoding framework that enforces a DSL's concrete syntax, abstract syntax, and well-formedness constraints jointly.
Large language models (LLMs) are increasingly used to generate software engineering artifacts written in domain-specific languages (DSLs).
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