Preferred-MedRECT-32B

Model Description

Preferred-MedRECT-32B is a finetuned model based on Qwen/Qwen3-32B, which has been optimized for medical error detection and correction tasks using LoRA (Low-Rank Adaptation).

The model is trained on bilingual (Japanese/English) medical reasoning data with explicit reasoning processes, enabling it to detect errors, extract erroneous sentences, and provide corrections in clinical texts.

The model is released under the Apache License 2.0.

Model Performance

The table below shows cross-lingual performance comparison on MedRECT-ja (Japanese) and MedRECT-en (English) benchmarks. MedRECT evaluates models on three subtasks: error detection (F1), sentence extraction (Acc.), and error correction (EC Avg. Score).

Model	MedRECT-ja Error Det. F1	MedRECT-ja Sent. Ext. Acc.	MedRECT-ja EC Avg. Score	MedRECT-en Error Det. F1	MedRECT-en Sent. Ext. Acc.	MedRECT-en EC Avg. Score
Preferred-MedRECT-32B	0.743	81.5%	0.627	0.728	90.9%	0.718
Qwen3-32B (think)	0.723	72.5%	0.549	0.740	83.5%	0.550
gpt-oss-120b (medium)	0.721	77.4%	0.581	0.777	88.1%	0.630
gpt-oss-20b (medium)	0.718	64.3%	0.543	0.762	87.2%	0.590
GPT-4.1	0.658	52.6%	0.655	0.789	72.8%	0.710

Training Details

• Base Model: unsloth/Qwen3-32B
• Fine-tuning Method: LoRA (Low-Rank Adaptation)
• Training Data:
  • Japanese: 5,538 samples from JMLE (2018-2023)
  • English: 2,439 samples from MEDEC MS Subset
  • All samples include reasoning processes generated by DeepSeek-R1-0528

Limitations

The model was developed for research purposes and is not intended for clinical diagnosis. It is the users' responsibility to ensure compliance with applicable rules and regulations.

Contributors

Preferred Networks, Inc.

• Naoto Iwase
• Hiroki Okuyama
• Junichiro Iwasawa

Publications

Detailed evaluation results will be given in the research paper (to be released).

Citations

@article{medrect2025,
      title={MedRECT: A Medical Reasoning Benchmark for Error Correction in Clinical Texts},
      author={Iwase, Naoto and Okuyama, Hiroki and Iwasawa, Junichiro},
      journal={arXiv preprint (to be released)},
      year={2025}
}

License

Apache License 2.0