How does the GPT model follow the prompts and guidance?

GPT (Generative Pre-trained Transformer) is a pre-trained language model based on the Transformer model. Its main purpose is to generate natural language text. In GPT, the process of following prompts is called Conditional Generation, which means that given some prompt text, GPT can generate text related to these prompts. The GPT model learns language patterns and semantics through pre-training, and then uses this learned knowledge when generating text. In the pre-training stage, GPT is trained through large-scale text data and learns the statistical characteristics, grammatical rules and semantic relationships of vocabulary. This enables GPT to reasonably organize the language when generating text to make it coherent and readable. In conditional generation, we can give one or more prompt texts as the basis for generating text. For example, given a question as a prompt, GPT can generate answers relevant to the question. This approach can be applied to many natural language processing tasks, such as machine translation, text summarization, and dialogue generation. In short

1. Basic concepts

Before introducing how to follow the prompts of the GPT model, you need to understand some basic concepts first.

1. Language model

Language model is used to probability model natural language sequences. Through the language model, we can calculate the probability value of a given sequence under the model. In the field of natural language processing, language models are widely used in multiple tasks, including machine translation, speech recognition, and text generation. The main goal of a language model is to predict the probability of the next word or character, based on the words or characters that have appeared before. This can be achieved through statistical methods or machine learning techniques such as neural networks. Statistical language models are usually based on n-gram models, which assume that the occurrence of a word is only related to the previous n-1 words. Language models based on neural networks, such as recurrent neural networks (RNN) and Transformer models, can capture longer contextual information, thereby improving the performance of the model

2. Pre-training model

The pre-training model refers to a model that is trained unsupervised on large-scale text data. Pre-trained models usually adopt self-supervised learning, which uses contextual information in text data to learn language representation. Pre-trained models have achieved good performance in various natural language processing tasks, such as BERT, RoBERTa, and GPT.

3.Transformer model

The Transformer model is a neural network model based on the self-attention mechanism, proposed by Google in 2017. The Transformer model has achieved good results in tasks such as machine translation. Its core idea is to use a multi-head attention mechanism to capture contextual information in the input sequence.

2. GPT model

The GPT model is a pre-trained language model proposed by OpenAI in 2018. Its core is based on Transformer The architecture of the model. The training of the GPT model is divided into two stages. The first stage is self-supervised learning on large-scale text data to learn language representation. The second stage is fine-tuning on specific tasks, such as text generation, sentiment analysis, etc. The GPT model performs well in text generation tasks and is able to generate natural and smooth text.

3. Conditional generation

In the GPT model, conditional generation refers to the generation and prompting given some prompt text. Related text. In practical applications, prompt text usually refers to some keywords, phrases or sentences, which are used to guide the model to generate text that meets the requirements. Conditional generation is a common natural language generation task, such as dialogue generation, article summarization, etc.

4. How the GPT model follows the prompts

When the GPT model generates text, it will predict the probability of the next word based on the input text sequence. Distribution, and samples according to the probability distribution to generate the next word. In conditional generation, the prompt text and the text to be generated need to be spliced ​​together to form a complete text sequence as input. Here are two common ways how GPT models follow prompts.

1. Prefix matching

Prefix matching is a simple and effective method, which is to splice the prompt text in front of the generated text to form a Complete text sequence as input. During training, the model learns how to generate subsequent text based on previous text. At generation time, the model generates prompt-related text based on the prompt text. The disadvantage of prefix matching is that the position and length of the prompt text need to be manually specified, which is not flexible enough.

2. Conditional input

#Conditional input is a more flexible method, that is, the prompt text is used as a conditional input, and each generated text is time steps are input into the model together. During training, the model will learn how to generate text that meets the requirements based on the prompt text. When generating, you can arbitrarily specify the content and location of the prompt text to generate text related to the prompt. The advantage of conditional input is that it is more flexible and can be adjusted according to specific application scenarios.

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