Small Language Models Learn Enhanced Reasoning Skills from Medical Textbooks
arxiv(2024)
摘要
While recent advancements in commercial large language models (LM) have shown
promising results in medical tasks, their closed-source nature poses
significant privacy and security concerns, hindering their widespread use in
the medical field. Despite efforts to create open-source models, their limited
parameters often result in insufficient multi-step reasoning capabilities
required for solving complex medical problems. To address this, we introduce
Meerkat-7B, a novel medical AI system with 7 billion parameters. Meerkat-7B was
trained using our new synthetic dataset consisting of high-quality
chain-of-thought reasoning paths sourced from 18 medical textbooks, along with
diverse instruction-following datasets. Our system achieved remarkable accuracy
across seven medical benchmarks, surpassing GPT-3.5 by 13.1
outperforming the previous best 7B models such as MediTron-7B and BioMistral-7B
by 13.4
the United States Medical Licensing Examination (USMLE) for the first time for
a 7B-parameter model. Additionally, our system offered more detailed free-form
responses to clinical queries compared to existing 7B and 13B models,
approaching the performance level of GPT-3.5. This significantly narrows the
performance gap with large LMs, showcasing its effectiveness in addressing
complex medical challenges.
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