Comparison of techniques for fine-tuning open-weight models for entity extraction from radiology reports
Fine-tuned Gemma-3-12B matches GPT-4o on hemorrhage extraction when distilled from real reports.
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Fine-tuned Gemma-3-12B matches GPT-4o on hemorrhage extraction when distilled from real reports.
PersonaDose calibrates activation steering so models express requested persona intensities.

Google Research and partners introduce ToolGrad, a verified tool-chain-first data generation framework reaching 99.8% pass rate and boosting Gemma-3-12B to 83.1 on BFCL.
Researchers analyze why Preventative Steering protects LLMs against malicious fine-tuning, finding active adaptation drives protection, and propose Progressive Intensity Scheduling.
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