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The article proposes that type diversity in datasets explains why Transformers struggle with structural compositional generalisation, and validates this by varying diversity in COGS and SLOG datasets.
This research paper examines how large language models contribute to the decline of linguistic diversity, highlighting potential cultural and societal risks.
This paper presents Structure-Guided Entity Resolution (SGER), a framework that fine-tunes LLMs through curriculum learning for robust person name matching in linguistically diverse contexts, achieving 99.02% accuracy on Indian identity data and deployed at Dream11.