Cross Lingual Natural Language Inference

Cross-lingual natural language inference (XNLI) focuses on determining the logical relationship (entailment, contradiction, or neutral) between sentences in different languages, aiming to bridge the gap in natural language understanding across linguistic boundaries. Current research emphasizes improving the performance of multilingual large language models (MLLMs) on XNLI, particularly for low-resource languages, through techniques like model pruning, knowledge distillation, and soft prompting, often incorporating bilingual dictionaries or leveraging existing monolingual resources. These advancements are crucial for building more inclusive and robust NLP systems capable of handling diverse languages and facilitating cross-cultural communication and information access.