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Are Human Conversations Special? A Large Language Model Perspective
This study analyzes changes in the attention mechanisms of large language models (LLMs) when used to understand natural conversations between humans (human-human).
Tuned Contrastive Learning
A recent state-of-the-art, supervised contrastive (SupCon) loss, extends self-supervised contrastive learning to supervised setting by generalizing to multiple positives and negatives in a batch and improves upon the cross-entropy loss.
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