As large language models become more prevalent, their possible harmful or inappropriate responses are a cause for concern.
Models for text generation have become focal for many research tasks and especially for the generation of sentence corpora.
However, group chat is characterized by intertwined conversations and 'always on' availability, making it hard for users to pinpoint answers to questions they care about in real-time or search for answers in retrospective.
Data balancing is a known technique for improving the performance of classification tasks.
Based on recent advances in natural language modeling and those in text generation capabilities, we propose a novel data augmentation method for text classification tasks.
no code implementations • • Noam Slonim, Ehud Aharoni, Carlos Alzate, Roy Bar-Haim, Yonatan Bilu, Lena Dankin, Iris Eiron, Daniel Hershcovich, Shay Hummel, Mitesh Khapra, Tamar Lavee, Ran Levy, Paul Matchen, Anatoly Polnarov, Vikas Raykar, Ruty Rinott, Amrita Saha, Naama Zwerdling, David Konopnicki, Dan Gutfreund