用教育理论优化大模型，让AI更懂教书
WisdomBot: Tuning Large Language Models with Artificial Intelligence Knowledge
- 基于布卢姆分类法构建教学指令数据，引导模型生成符合教育逻辑的回答
- 推理时引入本地知识库与搜索引擎检索增强，提升事实类问题准确性
- 适合作为智能教学助手的底层模型，尤其适合中文教育场景
大语言模型（LLMs）在自然语言处理中展现出巨大潜力，预示着人工通用智能（AGI）的前景。然而，由于教育领域对专业知识、个性化学习和复杂概念简明解释的特殊需求，现有模型表现仍不理想。为此，本文提出专用于教育领域的新型大语言模型WisdomBot，融合大语言模型能力与教育理论，实现其在教育场景中的无缝集成。具体而言，我们基于布卢姆分类法（Bloom's Taxonomy）指导，构建自生成的教学知识概念与指令数据作为训练依据。为进一步提升模型在事实性问题上的准确性和专业性，我们在推理阶段引入两项关键增强：本地知识库检索增强与搜索引擎检索增强。通过在多个中文LLM上应用该方法，验证了微调后模型能生成更可靠、专业的回答。
原文摘要 · Abstract (English)
Large language models (LLMs) have emerged as powerful tools in natural language processing (NLP), showing a promising future of artificial generated intelligence (AGI). Despite their notable performance in the general domain, LLMs have remained suboptimal in the field of education, owing to the unique challenges presented by this domain, such as the need for more specialized knowledge, the requirement for personalized learning experiences, and the necessity for concise explanations of complex concepts. To address these issues, this paper presents a novel LLM for education named WisdomBot, which combines the power of LLMs with educational theories, enabling their seamless integration into educational contexts. To be specific, we harness self-instructed knowledge concepts and instructions under the guidance of Bloom's Taxonomy as training data. To further enhance the accuracy and professionalism of model's response on factual questions, we introduce two key enhancements during inference, i.e., local knowledge base retrieval augmentation and search engine retrieval augmentation during inference. We substantiate the effectiveness of our approach by applying it to several Chinese LLMs, thereby showcasing that the fine-tuned models can generate more reliable and professional responses.
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