Background
The performance of large models in the rare disease domain is limited by the training data coverage.Baichuan-M2-32B provides a path to improve this pain point through a mid-term training mechanism and a validator system.
Core Programs
- Three stages of knowledge infusion::
1. Preparation phase: collect rare disease guidelines/expert consensus PDF 2. Conversion phase: use LLM to convert documents into Q&A pairs 3. Injection phase: update model parameters through mid-training mechanism - Dynamic Validation Enhancements::
Adding rare disease test cases to the patient simulator and targeting supplemental training data based on the Knowledge Gap Report from the Validator system - mixed reasoning strategy::
Automatically switches to "caution mode" when rare disease keywords are triggered: a) Outputs a confidence statement b) Provides a link to the latest literature search c) Explicitly suggests a referral specialist
Implementation of recommendations
It is recommended that medical institutions establish a local rare disease knowledge base, form a linked diagnostic system through APIs and models, and form a workflow of "AI initial screening + expert review".
This answer comes from the articleBaichuan-M2: A Large Language Model for Augmented Reasoning in HealthcareThe
May not be reproduced without permission:AI productivity tools " How to address the knowledge limitations of Baichuan-M2-32B in rare disease diagnosis?
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