This AI Paper Explores the Impact of Model Compression on Subgroup Robustness in BERT Language Models

The significant computational demands of large language models (LLMs) have hindered their adoption across various sectors. This hindrance has shifted attention towards compression techniques designed to reduce the model size and computational needs without major performance trade-offs. This pivot is crucial in Natural Language Processing (NLP), facilitating applications from document classification to advanced conversational agents. […]

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