Bailicai: A Domain-Optimized Retrieval-Augmented Generation Framework for Medical Applications
<|reference_start|>Bailicai: A Domain-Optimized Retrieval-Augmented Generation Framework for Medical Applications: Large Language Models (LLMs) have exhibited remarkable proficiency in natural language understanding, prompting extensive exploration of their potential applications across diverse domains. In the medical domain, open-source LLMs have demonstrated moderate efficacy following domain-specific fine-tuning; however, they remain substantially inferior to proprietary models such as GPT-4 and GPT-3.5. These open-source models encounter limitations in the comprehensiveness of domain-specific knowledge and exhibit a propensity for 'hallucinations' during text generation. To mitigate these issues, researchers have implemented the Retrieval-Augmented Generation (RAG) approach, which augments LLMs with background information from external knowledge bases while preserving the model's internal parameters. However, document noise can adversely affect performance, and the application of RAG in the medical field remains in its nascent stages. This study presents the Bailicai framework: a novel integration of retrieval-augmented generation with large language models optimized for the medical domain. The Bailicai framework augments the performance of LLMs in medicine through the implementation of four sub-modules. Experimental results demonstrate that the Bailicai approach surpasses existing medical domain LLMs across multiple medical benchmarks and exceeds the performance of GPT-3.5. Furthermore, the Bailicai method effectively attenuates the prevalent issue of hallucinations in medical applications of LLMs and ameliorates the noise-related challenges associated with traditional RAG techniques when processing irrelevant or pseudo-relevant documents.<|reference_end|>
@article{long2024bailicai:,
    title={Bailicai: A Domain-Optimized Retrieval-Augmented Generation Framework
  for Medical Applications},
    author={Cui Long, Yongbin Liu, Chunping Ouyang, Ying Yu},
    journal={arXiv preprint arXiv:2407.21055},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.21055},
    primaryClass={cs.CL}
}