user: Classify each job as a "blue collar" or "white collar" job: auditor, electrician, plumber, data scientist, financial analyst, construction worker

assistant: Auditor: White Collar
Electrician: Blue Collar
Plumber: Blue Collar
Data Scientist: White Collar
Financial Analyst: White Collar
Construction Worker: Blue Collar

user: Classify each of the following as either round or square shaped: a planet, a a ball, a slice of bread, a chess board.

assistant: Planets are round.
Balls are round.
A slice of bread is square shaped.
A chess board is square shaped.

user: The paper "Attention is all you need" proposed the Transformer model for the first time, which had a profound impact on the subsequent LLM. Please give the main core idea of Transformer described in this paper

assistant: This paper is by Vaswani, Ashish & Shazeer, Noam & Parmar, Niki & Uszkoreit, Jakob & Jones, Llion & Gomez, Aidan & Kaiser, Lukasz & Polosukhin, Illia. and published in 2017. It laid the foundation for the subsequent GPT series of models and BERT series of models. The GPT model uses the decoder part of Transfomer, while the BERT model uses the encoder part of Transfomer. The main core architecture of the Transformer model includes: Self-Attention Mechanism, Positional Encoding, Encoder-Decoder Architecture, Multi-Head Attension.
Transformer effectively solves the difficult parallel problem of the previous RNN/LSTM model by using the Attention mechanism. Using Self-Attention solves the problem of limited labeled data in NLP. The sequence information in the input text is preserved through Positional Encoding. And Multi-Head Attention allows Transformer to learn different patterns of training data similar to Channel concept in CNN model.