The powerful generative pretrained transformer (GPT) language model introduced by OpenAI opens up a new field of natural language processing (NLP). Integrating GPT models into virtual assistants and chatbots enhances their capabilities, leading to a surge in demand for GPT models. According to a report titled "Global NLP Market" released by AlliedMarketResearch, the global NLP market size was US$11.1 billion in 2020 and is expected to reach US$341.5 billion by 2030, with a CAGR of 40.9% from 2021 to 2030 .
GPT models are a collection of deep learning-based language models created by the OpenAI team. Without supervision, these models can perform various NLP tasks such as question answering, textual entailment, text summarization, etc.
The most trained GPT model - GPT-4, with more than 1 trillion learning parameters, is more than ten times more powerful than any language model. Its advantage over other models is that it can perform the task without extensive tuning; it requires very little textual interaction presentation and the model does the rest. Advanced trained GPT models can make life easier by performing language translation, text summarization, question answering, chatbot integration, content generation, sentiment analysis, named entity recognition, text classification, text completion, text-to-speech synthesis and more .
What is the GPT model?
GPT stands for GenerativePre-trainedTransformer, the first general language model in NLP. Previously, language models were designed for single tasks such as text generation, summarization, or classification. GPT is the first general-purpose language model in the history of natural language processing that can be used for various NLP tasks. Let us now explore the three components of GPT, Generative, Pre-Trained, and Transformer, and understand what they mean.
Generative: Generative models are statistical models used to generate new data. These models can learn the relationship between variables in a dataset to generate new data points that are similar to those in the original dataset.
Pre-trained: These models have been pre-trained using large datasets and can be used when it is difficult to train new models. Although a pretrained model may not be perfect, it can save time and improve performance.
Transformer: The Transformer model is an artificial neural network, created in 2017, and is the most famous deep learning model capable of processing sequential data such as text. Many tasks such as machine translation and text classification are performed using transformer models.
GPT can perform various NLP tasks with high precision based on the large datasets it is trained on and its billion-parameter architecture, enabling it to understand logical connections in data. GPT models, such as the latest version of GPT-3, have been pretrained using text from five large datasets, including CommonCrawl and WebText2. The corpus contains nearly a trillion words, enabling GPT-3 to quickly perform NLP tasks without any data examples.
How the GPT model works
GPT is an AI language model based on transformer architecture, which is pre-trained, generative, unsupervised, and able to perform well in zero/once/few multitasking settings. It predicts the next token (instance of a sequence of characters) from a sequence of tokens used for NLP tasks, for which it has not been trained. After seeing only a few examples, it can achieve the desired results on some benchmarks, including machine translation, question answering, and cloze tasks. The GPT model mainly calculates the possibility of a word appearing in another text based on conditional probability. For example, in the sentence "Margaretis organizing agarage sale...perhaps we could purchase that old..." the word chair is more appropriate than the word "elephant". Also, the transformer model uses multiple units called attention blocks to learn which parts of the text sequence to focus on. A transformer may have multiple attention modules, each learning a different aspect of a language.
The Transformer architecture has two main parts: the encoder, which primarily operates on the input sequence, and the decoder, which operates on the target sequence and predicts the next item during training. For example, a converter might take a sequence of English words and predict the French word in the correct translation until it is done.
The encoder determines which parts of the input should be emphasized. For example, an encoder could read a sentence like "Thequickbrownfoxjumped". It then computes an embedding matrix (embeddings in NLP allow words with similar meanings to have similar representations) and converts it into a series of attention vectors. Now, what is an attention vector? You can think of the attention vector in a Transformer model as a special calculator that helps the model understand which parts of any given information are most important for making a decision. Suppose you are asked multiple questions on an exam and you have to answer them using different pieces of information. Attention vectors help you select the most important information to answer each question. In the case of the transformer model, it works the same way.
A multi-head attention block initially produces these attention vectors. They are then normalized and passed into a fully connected layer. Normalized again before passing to the decoder. During training, the encoder works directly on the target output sequence. Suppose the target output is the French translation of the English sentence "Thequickbrownfoxjumped". The decoder computes a separate embedding vector for each French word in the sentence. In addition, position encoders are applied in the form of sine and cosine functions. In addition, masked attention is used, which means that the first word of the French sentence is used, while all other words are masked. This allows the converter to learn to predict the next French word.
Meanwhile, the GPT model employs some data compression while consuming millions of sample texts, converting words into vectors that are just numerical representations. A language model then decompresses the compressed text into human-friendly sentences. Improved model accuracy by compressing and decompressing text. This also allows it to calculate the conditional probability of each word. GPT models can perform well in the "few shots" setting and respond to previously seen text samples. They only need a few examples to generate relevant responses because they have been trained on many text samples.
In addition, the GPT model has many capabilities, such as generating synthetic text samples of unprecedented quality. If you start the model with an input, it will generate a long continuation. GPT models outperform other language models trained on domains such as Wikipedia, news, and books without using domain-specific training data. GPT only learns language tasks such as reading comprehension, summarization, and question answering from text, without task-specific training data. The scores for these tasks ("score" refers to the numerical value assigned by the model to represent the likelihood or probability of a given output or outcome) are not the best, but they suggest using unsupervised techniques with enough data and computation to make the task benefit.
In GTP, the importance of data labeling is that it is a key link in the process of graph transformation, which determines the accuracy and reliability of the input and output data of the transformation. Data annotation can help developers better understand the data structure and processing flow in the software, making the development and maintenance process more efficient and convenient. Therefore, the importance of data annotation to GTP cannot be ignored, and it is one of the keys to the design and implementation of GTP.
JLW Technology｜Data Collection｜Data Labeling
Helping artificial intelligence technology, empowering the intelligent transformation and upgrading of traditional industries