no code implementations • 8 Mar 2024 • Toshish Jawale, Chaitanya Animesh, Sekhar Vallath, Kartik Talamadupula, Larry Heck
This study analyzes changes in the attention mechanisms of large language models (LLMs) when used to understand natural conversations between humans (human-human).
no code implementations • 18 May 2023 • Chaitanya Animesh, Manmohan Chandraker
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.