Question answering is a natural language understanding task that involves reasoning over both explicit context and unstated, relevant domain knowledge. Large language models (LLMs), which underpin most contemporary question answering systems, struggle to induce how concepts relate in specialized domains such as medicine. Existing medical LLMs are also costly to train. In this work, we present MEG, a parameter-efficient approach for medical knowledge-augmented LLMs. MEG uses a lightweight mapping network to integrate graph embeddings into the LLM, enabling it to leverage external knowledge in a cost-effective way. We evaluate our method on four popular medical multiple-choice datasets and show that LLMs greatly benefit from the factual grounding provided by knowledge graph embeddings. MEG attains an average of +10.2% accuracy over the Mistral-Instruct baseline, and +6.7% over specialized models like BioMistral. We also show results based on Llama-3. Finally, we show that MEG's performance remains robust to the choice of graph encoder.

翻译：问答是一项自然语言理解任务，需要基于显式上下文和未明确陈述的相关领域知识进行推理。作为当前大多数问答系统基础的大语言模型，在医学等专业领域中难以推断概念间的关联。现有的医学大语言模型训练成本也较高。本工作中，我们提出了MEG——一种参数高效的医学知识增强大语言模型方法。MEG通过轻量级映射网络将图嵌入整合到大语言模型中，使其能够以经济高效的方式利用外部知识。我们在四个主流医学多选题数据集上评估了该方法，结果表明大语言模型能够显著受益于知识图谱嵌入提供的事实依据。相较于Mistral-Instruct基线模型，MEG平均准确率提升+10.2%；相较于BioMistral等专业模型，平均提升+6.7%。我们还展示了基于Llama-3的实验结果。最后，我们证明MEG的性能对图编码器的选择具有鲁棒性。