Implementing Retrieval-Augmented Generation (RAG) for Large Language Models to Build Confidence in Traditional Chinese Medicine

Many English-speaking individuals exhibit skepticism regarding the efficacy of traditional Chinese medicine (TCM), a bias often embedded in the training data of language models, leading to prejudiced outputs. Implementing Retrieval-Augmented Generation (RAG) within the Llama model provides a novel and significant approach to mitigating this bias through the integration of external, credible sources. The methodology involved collecting a diverse dataset, preprocessing and indexing it, and then integrating it with the Llama model to enhance response generation. Quantitative and qualitative analyses indicated significant improvements in confidence scores, sentiment balance, and content accuracy of TCM-related responses, demonstrating the effectiveness of RAG in reducing biases. The iterative fine-tuning process further refined the model's ability to produce more informed, balanced, and unbiased outputs. The study highlights the potential of RAG to enhance the fairness and reliability of language models, contributing to more equitable representations of culturally significant practices.