An empirical comparison finds that Mean pooling works best for BERT sentiment classification while Weighted Sum works best for GPT-2, though differences are small and reported without error bars.
GPT-3: Its nature, scope, limits, and consequences
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Comparative Analysis of Pooling Mechanisms in LLMs: A Sentiment Analysis Perspective
An empirical comparison finds that Mean pooling works best for BERT sentiment classification while Weighted Sum works best for GPT-2, though differences are small and reported without error bars.