Fine-Tuned Classification Engines

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

Results

• ~80% model-to-human coder agreement
• Weeks → hours turnaround compression
• Bilingual (English / Traditional Chinese) out of the box
• Auditable classification with full traceability