WisdomBot: Tuning Large Language Models with Artificial Intelligence Knowledge
This addresses the need for specialized, personalized educational AI tools, though it is incremental as it builds on existing LLMs with domain-specific enhancements.
The paper tackles the problem of large language models (LLMs) being suboptimal in education by introducing WisdomBot, a novel LLM that integrates educational theories and retrieval augmentations, resulting in more reliable and professional responses as demonstrated on Chinese LLMs.
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.