Building a large natural language inference dataset manually is a time-consuming task. The natural language inference dataset is composed of three classes: entailment, contradiction, and neutral. When building a Vietnamese NLI dataset, entailment samples may be collected from Vietnamese news web pages to reduce annotation costs. If the contradictions can be automatically generated, the cost will be reduced more. Therefore, a process has been proposed for building a Vietnamese NLI dataset. This process employed a high-quality large language model to generate contradiction samples. To verify if this process significantly helps, the TRAIN set and the TEST set were built using this process. The experiments showed that a large language model was useful to build a Vietnamese NLI dataset by manually filtering incorrect sentences. When using a large language model without manually filtering incorrect sentences, the quality of the dataset is low.