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.
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 arXiv:2511.00421},
year={2025}
}