Comparison of techniques for fine-tuning open-weight models for entity extraction from radiology reports

arXiv:2609.40236 · cs.CL, cs.LG · Submitted 2026-09-30 · Read on arXiv

cs.CL, cs.LG

Submitted: 2026-09-30

Updated: 2026-09-30

Terminology

• Fine-Tuning — 21× in this paper · explained in 5 episodes
• Adaptation — 10× in this paper · explained in 1 episode
• AUROC — 7× in this paper · explained in 2 episodes
• Distillation — 7× in this paper · explained in 2 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• Calibration — 4× in this paper · explained in 7 episodes
• LoRA Adapters — 3× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Hallucination — 2× in this paper · explained in 3 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Annotator Agreement — 1× in this paper · explained in 1 episode
• Distribution Shift — 1× in this paper · explained in 5 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• National Institutes of Health — 1× in this paper
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
• CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT
• Large Language Model-Based Uncertainty-Adjusted Label Extraction for Artificial Intelligence Model Development in Upper Extremity Radiography
• How is ChatGPT's behavior changing over time?
• Distilling Large Language Models for Efficient Clinical Information Extraction
• The False Promise of Imitating Proprietary LLMs
• Statistically Valid Post-Deployment Monitoring Should Be Standard for AI-Based Digital Health
• How to Synthesize Text Data without Model Collapse?
• On Calibration of Modern Neural Networks

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