Question-answering (QA) is an important application of Information Retrieval (IR) and language models, and the latest trend is toward pre-trained large neural networks with embedding parameters. Augmenting QA performances with these LLMs requires intensive computational resources for fine-tuning. We propose an innovative approach to improve QA task performances by integrating optimized vector retrievals and instruction methodologies. Based on retrieval augmentation, the process involves document embedding, vector retrieval, and context construction for optimal QA results. We experiment with different combinations of text segmentation techniques and similarity functions, and analyze their impacts on QA performances. Results show that the model with a small chunk size of 100 without any overlap of the chunks achieves the best result and outperforms the models based on semantic segmentation using sentences. We discuss related QA examples and offer insight into how model performances are improved within the two-stage framework.

翻译：问答（QA）是信息检索（IR）与语言模型的重要应用领域，当前趋势正朝着采用嵌入参数的预训练大型神经网络发展。利用这些大语言模型（LLMs）提升QA性能通常需要大量计算资源进行微调。本文提出一种创新方法，通过整合优化的向量检索与指令策略来改善QA任务性能。该方法基于检索增强机制，涵盖文档嵌入、向量检索以及面向最优QA结果的上下文构建过程。我们实验了不同文本切分技术与相似度函数的组合，并分析了它们对QA性能的影响。结果表明，采用无重叠的100词小文本块切分的模型取得了最佳效果，其性能优于基于句子语义切分的模型。我们讨论了相关QA示例，并对两阶段框架中模型性能的提升机制提供了深入见解。