Fine-Tuned Classification Engines
Multilingual NLP · XLM-RoBERTa · LoRA/QLoRA
Manual coding of open-ended survey responses is one of the most time-consuming bottlenecks in market research and academic analysis. Traditional approaches require human coders to read, interpret, and categorize thousands of responses — a process that takes weeks, suffers from inter-coder variability, and does not scale across languages.
Solution
A production multilingual text classification pipeline built on XLM-RoBERTa-Large with LoRA (Low-Rank Adaptation) fine-tuning. The system processes open-ended survey responses in both English and Traditional Chinese, achieving approximately 80% human-level agreement while compressing turnaround from weeks to hours.
Key Capabilities
- Cross-lingual transfer learning using XLM-RoBERTa-Large
- Parameter-efficient fine-tuning via LoRA/QLoRA adapters
- Legacy codeframe matching for longitudinal study continuity
- Calibrated confidence scores with human-in-the-loop review thresholds
Architectural Decisions
Why fine-tuning over prompt-engineering or RAG?The decision was driven by four constraints that ruled out API-based approaches. First, client data privacy requirements prohibited sending survey responses to external inference APIs. Second, at the volume of hundreds of thousands of responses per wave, per-token API costs exceeded the fixed cost of hosting a fine-tuned model. Third, inference latency needed to stay under a few seconds per batch to compress turnaround from weeks to hours — a constraint that ruled out multi-step prompt chains. Fourth, the ~80% agreement target required the model to internalize the codeframe's categorization logic, which few-shot prompting at scale could not consistently reproduce. LoRA/QLoRA gave us parameter-efficient fine-tuning without the memory cost of full model fine-tuning, and XLM-RoBERTa's cross-lingual checkpoint meant a single model handled both English and Traditional Chinese out of the box.
Results
- ~80% model-to-human coder agreement
- Weeks → hours turnaround compression
- Bilingual (English / Traditional Chinese) out of the box
- Auditable classification with full traceability