SLM Finetuning for Natural Language to Domain Specific Code Generation in Production
Many applications today use large language models for code generation; however, production systems have strict latency requirements that can be difficult to meet with large models. Small language models with a few billion parameters are resource efficient but may suffer from limited reasoning, hallucinations, or poor retention of longer context. Fine tuning improves task specific accuracy by embedding domain knowledge directly into model weights, reducing reliance on runtime context. We previously implemented a baseline natural language to code generation approach using a retrieval augmented generation pipeline that dynamically selected few shot examples to embed domain specific language ...
| Authors | Renjini R. Nair, Damian K. Kowalczyk, Marco Gaudesi et al. |
|---|---|
| Primary category | cs.LG |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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