ChatGPT adopts the route of "big data + large computing power + strong algorithm = large model" on the technical path, and explores a new paradigm in the direction of "basic large model + instruction fine-tuning", in which the basic model is similar to the brain, and the instruction fine-tuning is interactive training, and the two are combined to achieve language intelligence close to human.
ChatGPT applies the training method of "reinforcement learning based on human feedback", using human preferences as reward signals to train the model, making the model more and more in line with the human cognitive understanding model.
ChatGPT uses a variety of technologies to achieve its functions, mainly including:
1.Transformer model: This is the basic structure of ChatGPT, which is a neural network structure based on attention mechanism, which can be computed and modeled in parallel and long-distance dependencies, which is very suitable for language understanding and generation tasks.
The Transformer model is a deep learning model, and its biggest feature is that it abandons the traditional CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network), and the entire network structure is completely composed of the Attention mechanism. The model consists of two parts: the encoding component and the decoding component, and its essence is an encoder-decoder architecture. Transformer models can be computed in parallel and can take into account the global information of the data, so they perform well in tasks that process large amounts of data and need to consider the global information of the data. However, the Transformer model is computationally intensive and has many training parameters.
2.GPT pre-trained model: ChatGPT uses GPT-3 released by OpenAI as its pre-trained model. GPT-3 is a large-scale language understanding model with 17.5 billion parameters that can understand hundreds of language tasks at the human level.
3.Seq2Seq model: This is an encoder-decoder model in which the encoder is used to understand the input, compressed into a vector of fixed dimensions, and the decoder is used to generate the output, based on which the individual elements of the target sequence are generated one by one. This model structure is suitable for sequence-to-sequence tasks such as translation, dialogue, etc.
4.A self-attention mechanism is an attention mechanism that allows the model to focus its attention on the relationships between different parts of the input data as it processes the input data. This mechanism is particularly useful in neural networks, especially when working with sequential data such as text, time series, etc.
In the self-attention mechanism, the model assigns a weight to each part of the input data, which indicates how much attention the model pays to that part when generating the output. Weights are determined by calculating the similarity between different parts of the input data, with the higher the similarity, the greater the weight.
The core idea of the self-attention mechanism is to let the model learn the relationships between different parts of the input data on its own, without relying on external information or prior knowledge. This makes the model more flexible and powerful when dealing with complex tasks.
In practical applications, the self-attention mechanism has been widely used in various deep learning models, such as Transformer and BERT. These models have achieved remarkable success in natural language processing, speech recognition, image recognition, and other fields.