Recent advances in Large Language Models (LLMs) have achieved remarkable
breakthroughs in understanding and responding to user intents. However, their
performance lag behind general use cases in some expertise domains, such as
Chinese medicine. Existing efforts to incorporate Chinese medicine into LLMs
rely on Supervised Fine-Tuning (SFT) with single-turn and distilled dialogue
data. These models lack the ability for doctor-like proactive inquiry and
multi-turn comprehension and cannot always align responses with safety and
professionalism experts. In this work, we introduce Zhongjing, the first
Chinese medical LLaMA-based LLM that implements an entire training pipeline
from pre-training to reinforcement learning with human feedback (RLHF).
Additionally, we introduce a Chinese multi-turn medical dialogue dataset of
70,000 authentic doctor-patient dialogues, CMtMedQA, which significantly
enhances the model's capability for complex dialogue and proactive inquiry
initiation. We define a refined annotation rule and evaluation criteria given
the biomedical domain's unique characteristics. Results show that our model
outperforms baselines in various capacities and matches the performance of
ChatGPT in a few abilities, despite having 50x training data with previous best
model and 100x parameters with ChatGPT. RLHF further improves the model's
instruction-following ability and safety. We also release our code, datasets
and model for further research.

该研究论文介绍了一个基于中医的大型语言模型，通过从事前训练到强化学习与人类反馈（RLHF）的整个训练流程，以及引入一个包含 7 万个真实医患对话的中文多轮医疗对话数据集 CMtMedQA，该模型在多个方面超越了基线模型，并且通过对该模型的进一步改进来提高其指令遵循能力和安全性。

中荆：通过专家反馈和实际多轮对话提升大型语言模型的中医能力

Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language  Model through Expert Feedback and Real-world Multi-turn Dialogue

Virtual health has been acclaimed as a transformative force in healthcare
delivery. Yet, its dropout issue is critical that leads to poor health
outcomes, increased health, societal, and economic costs. Timely prediction of
patient dropout enables stakeholders to take proactive steps to address
patients' concerns, potentially improving retention rates. In virtual health,
the information asymmetries inherent in its delivery format, between different
stakeholders, and across different healthcare delivery systems hinder the
performance of existing predictive methods. To resolve those information
asymmetries, we propose a Multimodal Dynamic Knowledge-driven Dropout
Prediction (MDKDP) framework that learns implicit and explicit knowledge from
doctor-patient dialogues and the dynamic and complex networks of various
stakeholders in both online and offline healthcare delivery systems. We
evaluate MDKDP by partnering with one of the largest virtual health platforms
in China. MDKDP improves the F1-score by 3.26 percentage points relative to the
best benchmark. Comprehensive robustness analyses show that integrating
stakeholder attributes, knowledge dynamics, and compact bilinear pooling
significantly improves the performance. Our work provides significant
implications for healthcare IT by revealing the value of mining relations and
knowledge across different service modalities. Practically, MDKDP offers a
novel design artifact for virtual health platforms in patient dropout
management.

该论文提出了一种 Multimodal Dynamic Knowledge-driven Dropout Prediction (MDKDP) 框架，通过学习医生 - 患者对话和各利益相关者在在线和离线医疗服务中的动态网络中的隐式和显式知识，解决了虚拟健康领域中的患者退治问题，极大地提高了患者留存率。