Learn ChatGPT from ChatGPT

Understand ChatGPT Technology#

ChatGPT is a conversational AI model developed by OpenAI. It’s based on the Transformer architecture and is trained on a large corpus of text data to generate human-like responses to text-based inputs. To understand ChatGPT, you should know the following basics:

1. Natural Language Processing (NLP): ChatGPT uses NLP techniques to understand and generate human language.
2. Machine learning: ChatGPT is a machine learning model that uses supervised learning to generate text responses.
3. Transformer architecture: This is a neural network architecture used in NLP tasks that was introduced in the 2017 paper “Attention is All You Need”.
4. Pre-training: ChatGPT is a pre-trained model that was fine-tuned on a large corpus of text data to generate text responses.
5. Fine-tuning: This is the process of training a pre-trained model on a smaller, specific task-specific dataset to adapt it to a particular use case.
6. Generative model: ChatGPT is a generative model, which means it generates text rather than simply classifying or translating it.
7. Language generation: ChatGPT uses probabilistic language generation techniques to generate text responses that are contextually relevant to the input prompt.

Attention is All you Need#

The authors argue that attention mechanisms are sufficient for building deep learning models for NLP tasks, such as machine translation, and that these models can be trained effectively in an end-to-end manner.

Self-attention mechanisms allow the model to selectively focus on different parts of the input sequence. This allows the model to process input sequences in parallel, which makes it faster and more parallelizable than RNNs.

The Transformer architecture also introduced the concept of multi-head attention, which allows the model to attend to different parts of the input sequence in multiple ways, and the position encoding mechanism, which encodes the relative position of words in a sentence.

GPT#

GPT stands for Generative Pretrained Transformer, which is a language generation model developed by OpenAI.

GPT uses a large corpus of text data to pre-train a deep neural network on the task of predicting the next word in a sentence. The pre-training allows the model to learn general language patterns and relationships, so that it can generate text that is similar to the training data.

GPT vs. Transformer#

• The Transformer architecture is a type of neural network that can be used for various NLP tasks.
• GPT is a specific pre-trained language generation model based on the Transformer architecture.

# Simple implementation of Transformer
import torch
import torch.nn as nn
import torch.nn.functional as F

class Transformer(nn.Module):
    def __init__(self, d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward, dropout=0.1):
        super(Transformer, self).__init__()

        self.encoder_layer = nn.TransformerEncoderLayer(d_model, nhead, dim_feedforward, dropout)
        self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer, num_encoder_layers)

        self.decoder_layer = nn.TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout)
        self.transformer_decoder = nn.TransformerDecoder(self.decoder_layer, num_decoder_layers)

    def forward(self, src, tgt, src_mask=None, tgt_mask=None, memory_mask=None, src_key_padding_mask=None, tgt_key_padding_mask=None, memory_key_padding_mask=None):
        memory = self.transformer_encoder(src, src_mask)
        output = self.transformer_decoder(tgt, memory, tgt_mask, memory_mask)
        return output

# Simple implementation of GPT-1
import torch
import torch.nn as nn
import torch.nn.functional as F

class GPT1(nn.Module):
    def __init__(self, vocab_size, d_model, nhead, num_layers, dim_feedforward, dropout=0.1):
        super(GPT1, self).__init__()

        self.embedding = nn.Embedding(vocab_size, d_model)
        self.transformer = nn.Transformer(d_model, nhead, num_layers, dim_feedforward, dropout)
        self.fc = nn.Linear(d_model, vocab_size)

    def forward(self, x):
        x = self.embedding(x)
        x = self.transformer(x)
        x = self.fc(x)
        return x

Text and Code Embeddings by Contrastive Pre-Training#

Paper:

The paper proposes a novel pre-training method for learning text and code embeddings. The main idea behind the method is to use a contrastive loss function to pre-train the embeddings in an unsupervised manner.

The loss function used for this prediction task is a contrastive loss function that encourages the embeddings of similar tokens (e.g., tokens with similar meanings) to be close together in the embedding space, while encouraging the embeddings of dissimilar tokens to be far apart.

Zero-shot learning is a machine learning approach where a model is trained to recognize and classify new, unseen classes without any additional training data.

Autoregressive model#

An autoregressive model is a type of statistical model that is used for time series forecasting and sequence generation tasks. In an autoregressive model, the current output or prediction is a function of previous inputs or outputs in the sequence.

The most common form of an autoregressive model is the Autoregressive Integrated Moving Average (ARIMA) model, which is used in time series analysis to model the dependencies between observations over time. ARIMA models can be used to perform various tasks, such as prediction of future values, identifying trends and patterns in time series data, and modeling the impact of past events on future outcomes.

In the field of machine learning and artificial neural networks, autoregressive models are used for sequence generation tasks, such as text generation and music composition.

Zero-Shot learning#

The goal of zero-shot learning is to learn a model that can generalize to new classes based on prior knowledge or information about the relationships between classes, without the need for additional labeled examples of those classes.

In zero-shot learning, a model is typically trained on a set of source classes and a semantic representation of each class is obtained.

Zero-shot learning is a challenging problem in machine learning, and it is particularly useful for applications where it is not feasible to collect labeled data for every class. For example, in computer vision, zero-shot learning can be used to recognize and classify new species of animals or plants without the need for labeled images of those species.

e.g. $\text{zebras} = \text{striped} + \text{horses}$

Not from ChatGPT

min GPT#

https://github.com/karpathy/minGPT/blob/master/mingpt/model.py

Pros and Cons#

• too generalized <–> more proper answers for everyone