Suppressing LLM-Generated DSL Hallucinations with Compiler Validation

As large language models (LLMs) are increasingly used for code generation, a problem has been identified: hallucinations—erroneous outputs—are particularly prone to occur when handling domain-specific languages (DSL). Engineer Irakli Betchvaia has proposed "Typed Domain Grounding" as an approach to address this challenge.

A DSL is a programming language designed specifically for a particular business or technical domain, with vocabularies and syntax more restricted than general-purpose languages. Because LLMs have limited training data for such niche languages, they tend to produce grammatically incorrect outputs or invent nonexistent commands. Typed Domain Grounding is based on the idea of "embedding" the DSL within a general-purpose, typed mainstream language, making it harder for LLMs to generate malformed structures.

To validate this approach, Betchvaia used the kUML benchmark and a concrete case study of Infrastructure as Code (IaC). Infrastructure as Code is a technique for describing and managing server and network configurations through code, widely used in cloud development. When LLMs generate incorrect configuration code in this domain, it risks system failures, making accuracy assurance critical.

A particularly notable mechanism is the "generate-compile-repair loop." First, an LLM generates code; next, a compiler (a tool that validates syntax when translating programs to machine language) detects errors; then, the error information is fed back to the LLM for correction. Through iteration, outputs that violate type system or compiler rules are automatically filtered out. The key feature is that mechanical validation acts as a quality gate without requiring manual review at each step.

The significance of this technique directly addresses a concern facing LLM deployment: the reliability of outputs. While LLMs are maturing in general-purpose code generation, they remain difficult to trust for specialized DSLs. By leveraging the type system as an existing software engineering asset, it may be possible to enhance reliability without requiring additional fine-tuning or retraining.

Looking forward, this kind of approach raises a broader question: how can LLMs be combined with existing development infrastructure (compilers, type checkers, test frameworks)? Rather than strengthening the model itself, the strategy of pairing it with surrounding validation mechanisms represents an important design philosophy, particularly for business systems and social infrastructure where errors are not easily tolerated.