While holding great promise for improving and facilitating healthcare, large language models (LLMs) struggle to produce up-to-date responses on evolving topics due to outdated knowledge or hallucination. Retrieval-augmented generation (RAG) is a pivotal innovation that improves the accuracy and relevance of LLM responses by integrating LLMs with a search engine and external sources of knowledge. However, the quality of RAG responses can be largely impacted by the rank and density of key information in the retrieval results, such as the "lost-in-the-middle" problem. In this work, we aim to improve the robustness and reliability of the RAG workflow in the medical domain. Specifically, we propose a map-reduce strategy, BriefContext, to combat the "lost-in-the-middle" issue without modifying the model weights. We demonstrated the advantage of the workflow with various LLM backbones and on multiple QA datasets. This method promises to improve the safety and reliability of LLMs deployed in healthcare domains.

翻译：尽管大型语言模型(LLM)在改进和促进医疗保健方面具有巨大潜力，但由于知识过时或产生幻觉，它们难以就发展中的主题生成最新回应。检索增强生成(RAG)是一项关键创新，通过将LLM与搜索引擎及外部知识源相结合，显著提升了LLM回应的准确性与相关性。然而，RAG回答的质量在很大程度上受检索结果中关键信息排序与密度的影响，例如"中间信息丢失"问题。本研究旨在提升医疗领域RAG工作流程的鲁棒性与可靠性。具体而言，我们提出了一种MapReduce策略——BriefContext，在不修改模型权重的情况下应对"中间信息丢失"问题。我们通过多种LLM骨干网络并在多个问答数据集上验证了该工作流程的优势。该方法有望提升医疗健康领域部署的LLM的安全性与可靠性。