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The paper Efficient Few-Shot Learning Without Prompts by researchers has gained attention for outperforming GPT-3 in text classification with 50 or fewer labeled examples. It is 1600 times smaller and can run on a CPU. The paper discusses recent few-shot methods like parameter fine-tuning and pattern-exploiting training, which have shown impressive results in low-resource settings. These methods are challenging to use due to variability from manually crafted prompts and the need for billion parameters.
