Domain-Adapted Small Language Models with Hybrid Post-Processing: Achieving Cost-Efficient, Low-Latency Multi-Label Structured Prediction via LoRA Fine-Tuning on Scarce Data

Deploying frontier large language models (LLMs) for domain-specific structured evaluation tasks often incurs substantial latency, cost, and data privacy overhead. We present a hybrid framework that combines a fine-tuned small language model (LLaMA 3.1 8B, with only 2.05% trainable parameters via LoRA) and a deterministic rule-based post-processing layer. Trained on just 219 curated examples, the system is applied to multi-label compliance evaluation of conversational transcripts spanning 18 heterogeneous output fields. In blind evaluation on 53 previously unseen production transcripts, it achieves 100% JSON structural validity, 83.0% human-validated overall accuracy, and 100% accuracy on the most critical classification field. The proposed approach formalizes a hybrid neural-symbolic decomposition and introduces targeted hard-negative augmentation to improve performance on critical decision boundaries. Running on a single NVIDIA A100 GPU, inference completes in approximately 2 seconds, which is 2-5x faster than frontier-model APIs. The system costs only $0.013 per evaluation compared with $0.025-$0.055 for proprietary alternatives, resulting in 46-76% cost savings. These results demonstrate that domain-adapted small language models, when combined with deterministic post-processing, can match frontier-model accuracy for structured compliance evaluation while substantially reducing operational cost, latency, and privacy risk. Keywords: small language models, parameter-efficient fine-tuning, LoRA, domain adaptation, hybrid inference, compliance evaluation, structured output.