Update README.md

README.md

@@ -16,10 +16,10 @@ While many works such as Alpaca, Belle, etc., are based on the self-instruct app
 <p align="center"><b>Fig 2. A Multi-task Therapeutic Behavior Decomposition Instruction Construction Strategy in the Loop of Human Physicians.</b></p>
 
 #### 1.1 Multi-task Therapeutic Behavior Decomposition Instruction Construction Strategy
-Human memory and understanding require the construction of various scenarios and stories to implicitly encode knowledge information. The clarity of memory depends on the duration and richness of the learning process. Interleaved learning, spaced practice, and diversified learning can enhance the consolidation of knowledge, thereby forming a deep understanding of domain knowledge. Our approach is to learn from the process of human memory knowledge, use professional tables, leverage the language representation capabilities of large language models, strictly set specific prompt templates, so that the model can generate 16 scenarios based on the table data of Chinese medicine gynecology prescriptions, including patient therapeutic story, diagnostic analysis, diagnosis treatment expected result, formula function, interactive story, patient therapeutic story, narrative medicine, tongue & pulse, therapeutic template making, critical thinking, follow up, prescription, herb dosage, case study, real-world problem, disease mechanism, etc., to promote the model's reasoning ability for prescription data and diagnostic thinking logic.
+Human memory and understanding require the construction of various scenarios and stories to implicitly encode knowledge information. The clarity of memory depends on the duration and richness of the learning process. Interleaved learning, spaced practice, and diversified learning can enhance the consolidation of knowledge, thereby forming a deep understanding of domain knowledge. Our approach is to learn from the process of human memory knowledge, use professional tables, leverage the language representation capabilities of large language models, strictly set specific prompt templates, so that the model can generate 15 scenarios based on the table data of Chinese medicine gynecology prescriptions, including patient therapeutic story, diagnostic analysis, diagnosis treatment expected result, formula function, interactive story, patient therapeutic story, narrative medicine, tongue & pulse, therapeutic template making, critical thinking, follow up, prescription, herb dosage, case study, real-world problem, disease mechanism, etc., to promote the model's reasoning ability for prescription data and diagnostic thinking logic.
 
 #### 1.1多任务诊疗行为分解instruction构建策略
-人类在记忆和理解时需要构建各种情景和故事，以隐式编码知识信息。记忆的清晰程度取决于学习的持续过程和丰富程度。穿插学习、间隔练习和多样化学习可以提升知识的巩固程度，由此形成深刻的领域知识理解能力。我们的思路是借鉴人类记忆知识的过程，采用专业表格，借助大语言模型的语言表征能力，严格设置特定的prompt模板，使得模型基于中医妇科方药表格数据生成包括患者治疗故事、诊断分析、诊断治疗预期结果、处方功用、互动故事、患者治疗故事、叙事医学、舌脉象、诊疗方案制定、批判性思维、随访、处方、药物用量、个例研究、真实世界问题、病因病机等16个场景，以促进模型对中医方药数据及诊断思维逻辑的推理能力。
+人类在记忆和理解时需要构建各种情景和故事，以隐式编码知识信息。记忆的清晰程度取决于学习的持续过程和丰富程度。穿插学习、间隔练习和多样化学习可以提升知识的巩固程度，由此形成深刻的领域知识理解能力。我们的思路是借鉴人类记忆知识的过程，采用专业表格，借助大语言模型的语言表征能力，严格设置特定的prompt模板，使得模型基于中医妇科方药表格数据生成包括患者治疗故事、诊断分析、诊断治疗预期结果、处方功用、互动故事、患者治疗故事、叙事医学、舌脉象、诊疗方案制定、批判性思维、随访、处方、药物用量、个例研究、真实世界问题、病因病机等15个场景，以促进模型对中医方药数据及诊断思维逻辑的推理能力。
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     {
         "instruction": "我对三元汤的全过程很好奇，能否从简介、病历、症状、诊断和治疗，以及结果讨论等方面给我详细介绍？",