With the rapid development of natural language processing, many language models have been invented for multiple tasks. One important task is information retrieval (IR), which requires models to retrieve relevant documents. Despite its importance in many real-life applications, especially in retrieval augmented generation (RAG) systems, this task lacks Vietnamese benchmarks. This situation causes difficulty in assessing and comparing many existing Vietnamese embedding language models on the task and slows down the advancement of Vietnamese natural language processing (NLP) research. In this work, we aim to provide the Vietnamese research community with a new benchmark for information retrieval, which mainly focuses on retrieval and reranking tasks. Furthermore, we also present a new objective function based on the InfoNCE loss function, which is used to train our Vietnamese embedding model. Our function aims to be better than the origin in information retrieval tasks. Finally, we analyze the effect of temperature, a hyper-parameter in both objective functions, on the performance of text embedding models.

本研究解决了越南信息检索缺乏基准的问题，提供了一个新的越南信息检索基准，聚焦于检索和重排名任务。此外，提出了一种基于InfoNCE损失函数的新目标函数，用于训练越南嵌入模型，以期在信息检索任务中表现更优。最终分析了温度这一超参数对文本嵌入模型性能的影响。

提升越南信息检索的学习目标与基准