LLATMU at #SMM4H-HeaRD 2026: Clinical Text Structuring with QLoRA-based Generation and Partial-Label TNM Classification

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Health & Wellbeing — Clinical Care & Medical Practice, Health & Medical Research · Depth: Expert, short

Summary

The LLATMU systems, developed by Eric Hsiao, Min-Hsuan Ku, and Hsuan-Lei Shao, were submitted to the #SMM4H-HeaRD 2026 shared tasks, focusing on two clinical text structuring challenges. For Task 4, dialogue-to-SOAP note generation, they employed a QLoRA-based Ministral-3B system, instruction-tuned with parameter-efficient adaptation, achieving an official blind test average score of 0.53, surpassing the task-wide mean and median. For Task 6, TNM staging classification from pathology reports, their approach involved a three-head classification problem utilizing BioClinical-ModernBERT-large, incorporating long-context encoding, class-weighted loss, and normalized partial-label training. This model attained a validation average macro-F1 of 0.9196 and outperformed the official baseline on a challenging tie-break test set. These results highlight the significance of robust data handling, stable fine-tuning, and task-appropriate supervision for effective clinical Natural Language Processing in practical, constrained environments.

Key takeaway

For NLP Engineers developing clinical text structuring solutions, prioritize robust data handling and stable fine-tuning. Consider instruction-tuning smaller LLMs like Ministral-3B with QLoRA for generation tasks, as shown by its 0.53 score. For classification, utilize BioClinical-ModernBERT-large. Implement multi-head classification, long-context encoding, and class-weighted loss. This can achieve high performance, like a 0.9196 macro-F1 for TNM staging. Your task-appropriate supervision will significantly improve practical clinical NLP system outcomes.

Key insights

Effective clinical NLP requires robust data handling, stable fine-tuning, and task-appropriate supervision.

Principles

• Parameter-efficient LLM adaptation is effective.
• Multi-head classification aids complex medical staging.
• Robust data handling is key for clinical NLP.

Method

Instruction-tuning LLMs with QLoRA for generation and multi-head classification with BioClinical-ModernBERT-large, long-context encoding, class-weighted loss, and partial-label training for clinical text structuring.

In practice

• Apply QLoRA to Ministral-3B for clinical note generation.
• Implement class-weighted loss for imbalanced clinical data.

Topics

• Clinical NLP
• Text Structuring
• QLoRA Fine-tuning
• TNM Staging
• Ministral-3B
• BioClinical-ModernBERT

Best for: Research Scientist, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.