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

• URL: http://arxiv.org/abs/2606.05781v2
• Date: Sat, 06 Jun 2026 01:31:00 GMT
• Title: 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
• Abstract summary: Large language models (LLMs) for domain-specific structured evaluation tasks incurs prohibitive latency, cost, and data-privacy overhead.<n>We present a hybrid framework that fine-tunes a small language model on only 219 curated examples and couples it with a deterministic rule-based postprocessing layer.<n>Our system achieves 100% structural validity, 83.0% human-validated overall accuracy, and 100% accuracy on the most critical classification field in blind evaluation on 53 unseen production transcripts.
• Score: 6.3745740668603075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deploying frontier large language models (LLMs) for domain-specific structured evaluation tasks incurs prohibitive latency, cost, and data-privacy overhead. We present a hybrid framework that fine-tunes a small language model (LLaMA 3.1 8B, 2.05% trainable parameters via LoRA) on only 219 curated examples and couples it with a deterministic rule-based postprocessing layer. Applied to multi-label compliance evaluation of conversational transcripts (18 heterogeneous output fields), our system achieves 100% JSON structural validity, 83.0% human-validated overall accuracy, and 100% accuracy on the most critical classification field in blind evaluation on 53 unseen production transcripts. On a single NVIDIA A100 GPU, inference completes in $\sim$2 seconds -- 2--5x faster than frontier APIs -- at USD 0.013 per evaluation versus USD 0.025--0.055 for proprietary alternatives, yielding 46--76% cost savings. We introduce targeted hard-negative augmentation for critical decision boundaries and formalize the hybrid neural-symbolic decomposition, demonstrating that domain-adapted small language models with postprocessing can match frontier model accuracy while dramatically reducing operational cost, latency, and privacy risk.

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