Unverified50% confidenceFactExact time
Medical large language models are typically trained on a mixture of two types of data: instructional data (such as textbooks) and clinical data (such as patient records).
1
Sources
50%
Confidence
Long-term
Relevance
9/23/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedWhile keeping the total number of training tokens constant, the study systematically adjusts the ratio of instructional to clinical data in order to isolate the effect of data type as a variable.75% similarUnverifiedInstructional data primarily improves knowledge-intensive tasks and offers limited benefit for clinical tasks.68% similarUnverifiedThe capabilities and behavior of large language models are deeply dependent on training data.67% similarUnverifiedData curriculum learning organizes training data from easy to difficult, allowing models to progressively acquire complex knowledge.66% similarUnverifiedThe research in this paper involves large-scale extraction and distillation of reasoning chains from closed-source large model APIs to provide high-quality data for reinforcement learning training of self-developed models.65% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/942670API
curl https://kongchang.com/api/v1/knowledge/claims/942670MCP
get_claim(id=942670)