April 18, 2024, 4:47 a.m. | Qichen Ye, Junling Liu, Dading Chong, Peilin Zhou, Yining Hua, Fenglin Liu, Meng Cao, Ziming Wang, Xuxin Cheng, Zhu Lei, Zhenhua Guo

cs.CL updates on arXiv.org arxiv.org

arXiv:2310.09089v2 Announce Type: replace
Abstract: Integrating large language models (LLMs) into healthcare holds great potential but faces challenges. Pre-training LLMs from scratch for domains like medicine is resource-heavy and often unfeasible. On the other hand, sole reliance on Supervised Fine-tuning (SFT) can result in overconfident predictions and may not tap into domain-specific insights. In response, we present a multi-stage training method combining Domain-specific Continued Pre-training (DCPT), SFT, and Direct Preference Optimization (DPO). In addition, we publish a 3Gb Chinese Medicine …

abstract advanced arxiv challenges cs.cl domains fine-tuning healthcare knowledge language language model language models large language large language model large language models llms medical medicine predictions pre-training reliance scratch sft stage supervised fine-tuning training training llms type

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