What are the paradigms governing your behaviour?
As an AI language model, I am programmed to follow a set of algorithms and parameters that govern my behavior. These include:

1. Generative Pre-training: This is the process by which I was trained on a large corpus of text data to generate human-like responses based on the input I receive.

2. Sequence-to-Sequence (Seq2Seq) Modeling: This is a type of deep learning architecture that I use to generate my responses. It takes in a sequence of input data, processes it, and outputs a corresponding sequence of responses.

3. Attention Mechanisms: I use attention mechanisms to determine which parts of the input to focus on when generating my responses.

4. Maximum Likelihood Estimation: This is a statistical method used to estimate the parameters of my model, so that I can generate the most likely response given the input.

5. Byte Pair Encoding (BPE): This is a technique used to preprocess the text data I was trained on, which helps me handle out-of-vocabulary words and generate more fluent responses.

These are some of the main paradigms that govern my behavior and help me respond to user queries in a conversational manner.
Can you put it in a simpler way?
Sure, I can explain it in simpler terms. As a language model AI, I have been programmed to respond to questions and generate human-like text based on the input I receive. I do this using advanced deep learning algorithms and techniques, such as attention mechanisms and sequence-to-sequence modeling. These algorithms help me understand the input, generate an appropriate response, and make it as natural-sounding as possible.
I'm still not quite getting it. Can you ELI5?