Bridging the Data Gap: LLM-Driven Synthetic Data Generation for Low-Resource Natural Language Inference

This thesis addresses the challenge of data scarcity in low-resource languages by leveraging the dual role of Large Language Models (LLMs) to both generate and annotate data. We evaluate the reliability of LLM-based annotations and examine the effectiveness of LLM-generated synthetic data in impr