← Back to articles
Researchers achieve 32.87 score on clinical QA task using two-stage QLoRA fine-tuning of Qwen3-4B model
arXiv cs.CL · April 17, 2026
AI Summary
- •QU-NLP team applies two-stage Quantised Low-Rank Adaptation (QLoRA) to Qwen3-4B loaded in 4-bit NF4 quantisation for the ArchEHR-QA 2026 shared task
- •System first trained on 30,000 samples from emrQA-MedSQuAD corpus for clinical domain knowledge, then on 20 annotated development cases for task-specific output
- •Subtask 3 (answer generation) achieves overall score of 32.87 with BLEU=9.42, ROUGE-L=27.04, SARI=55.42, BERTScore=43.00, and MEDCON=37.04
- •Subtask 4 (evidence alignment) uses weighted ensemble of BM25, TF-IDF, and fine-tuned cross-encoder to reach 67.16 micro-F1 score on 100-case test set
Related Articles
Large Language Models
Accenture partners with Avanade and Microsoft to deploy AI factory agents that enhance manufacturing operations through human-AI collaboration
Yahoo Finance AI·Apr 20, 2026
Large Language Models
Meta's new Muse Spark AI model outperforms Google's Gemini 3, raising questions about which tech giant offers better investment potential in 2026.
Yahoo Finance AI·Apr 20, 2026
Large Language Models
Mac Mini shortages emerge as AI developers rush to build cost-effective local AI agents, driving up demand and prices across the supply chain.
Semafor Tech·Apr 20, 2026
Large Language Models
AI agent builders struggle with cost tracking and observability, seeking better solutions for monitoring token spend and workflow expenses.
r/AI_Agents·Apr 20, 2026
Large Language Models
B2B companies seek middle ground between off-the-shelf AI agents and expensive in-house teams, with custom dev shops like Svitla Systems gaining traction for modular solutions.
r/AI_Agents·Apr 20, 2026
Stay ahead with AI news
Get curated AI news from 200+ sources delivered daily to your inbox. Free to use.
Get Started Free