Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Models
CoRR(2024)
摘要
Recent proprietary large language models (LLMs), such as GPT-4, have achieved
a milestone in tackling diverse challenges in the biomedical domain, ranging
from multiple-choice questions to long-form generations. To address challenges
that still cannot be handled with the encoded knowledge of LLMs, various
retrieval-augmented generation (RAG) methods have been developed by searching
documents from the knowledge corpus and appending them unconditionally or
selectively to the input of LLMs for generation. However, when applying
existing methods to different domain-specific problems, poor generalization
becomes apparent, leading to fetching incorrect documents or making inaccurate
judgments. In this paper, we introduce Self-BioRAG, a framework reliable for
biomedical text that specializes in generating explanations, retrieving
domain-specific documents, and self-reflecting generated responses. We utilize
84k filtered biomedical instruction sets to train Self-BioRAG that can assess
its generated explanations with customized reflective tokens. Our work proves
that domain-specific components, such as a retriever, domain-related document
corpus, and instruction sets are necessary for adhering to domain-related
instructions. Using three major medical question-answering benchmark datasets,
experimental results of Self-BioRAG demonstrate significant performance gains
by achieving a 7.2
open-foundation model with a parameter size of 7B or less. Overall, we analyze
that Self-BioRAG finds the clues in the question, retrieves relevant documents
if needed, and understands how to answer with information from retrieved
documents and encoded knowledge as a medical expert does. We release our data
and code for training our framework components and model weights (7B and 13B)
to enhance capabilities in biomedical and clinical domains.
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