Summary
The paradigm shift triggered by ChatGPT compelled us to redefine what an NLP task is. We saw that ChatGPT, like other LLM models, can perform tasks they were not trained for, including many SuperGLUE tasks through advanced emergence. We explored the outputs of the attention heads to bridge the gap between numerical calculations and producing sequences of words.
We then explored how to measure the performance of multi-task transformers. Transformers’ ability to obtain top-ranking results for downstream tasks is unique in NLP history. We went through the demanding SuperGLUE tasks that brought transformers up to the top ranks of the GLUE and SuperGLUE leaderboards.
BoolQ, CB, WiC, and the many other tasks we covered are by no means easy to process, even for humans. We went through an example of several downstream tasks that show the difficulty transformer models face in proving their efficiency.
Transformers have proven their value by outperforming the former...
