Text classification is the gateway drug of NLP — sentiment analysis, spam detection, topic labeling — and the task where transformers first proved their dominance over LSTMs. BERT (2018) set the template, but the real revolution came when instruction-tuned LLMs like GPT-4 and Llama 3 started matching fine-tuned classifiers zero-shot, threatening to make task-specific training obsolete. SST-2, AG News, and IMDB remain standard benchmarks, though the field increasingly cares about multilingual and low-resource performance where English-centric models still stumble. The open question: does a 70B parameter model doing classification via prompting actually beat a 100M fine-tuned encoder when you factor in latency and cost?
More difficult successor to GLUE with 8 challenging tasks. Designed to be hard for current models.
Leading models on SuperGLUE.
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2 datasets tracked for this task.
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