The big language model behind chatGPT

Large language models, or LLM for short, Chinese for large language models, are very powerful machine learning models that can understand and generate natural language. Big language models are trained on large-scale text and code datasets to learn patterns and relationships in the language. LLM has many application scenarios, common ones are language translation, sentiment analysis, and content generation.

• Language translation: LLM can be used to translate text from one language to another. For example: translate an article written in French into English.
• Sentiment analysis: Analyzes the emotional tendencies of text content. For example: Determine whether a user's review is positive or negative.
• Content generation: Generate poetry, code, music, news, etc. through LLM.

Here's what you can learn from this article

• Learn and understand the concept of LLM and its importance in natural language processing (NLP).
• Learn about several types of popular big language models, such as BERT, GPT-3, and T5
• Hugging Face interface for the Big Prophecy Model

What is a big language model

The Big Language Model, or LLM, is an artificial intelligence technology that can generate text that conforms to human habits and complete a wide variety of natural language processing tasks. LLM is trained in large-scale datasets and learns patterns and potential associations in existing languages, and the text content generated by LLM is very similar to that generated by humans themselves, and even difficult to distinguish.

Here are some specific scenarios

• Google Translate: Google Translate translates through LLM, translating one language into another. LLM learns patterns and associations of different languages in large-scale corpus, so that language translation tasks can be completed. It is the use of LLM that makes Google Translate the most accurate translation tool in the world.
• GPT-3: GPT-3 is a large language model developed by OpenAI to generate text, translate languages, and write a variety of content. GPT-3 has been used in many scenarios, such as chatbots, text generators, and creative writing tools.
• Bard: Bard is a large language model developed by Google AI to complete question-and-answer tasks, and Bard can even answer open-ended, challenging, and even bizarre questions.

How big language models are built

Large language models (LLM) are trained on large-scale corpus data that teaches the model to learn statistical relationships between words, sentences, and paragraphs. If you input a prompt or query to a large model, the large model generates a response that is relevant to the input context. A typical example is the GPT-3 model in ChatGPT, which understands human language and has the human knowledge learned from human language, so it can generate a variety of styles of content.

Although ChatGPT's capabilities are incredible, the principle is that large models have mastered special "grammars" to match input prompts, and these "grammars" are learned in large-scale corpora. For example, if you ask ChatGPT to write a poem about love, the model will know that the subject of the content to be generated next is love, and the content form is poetry.

The overall architecture of the big language model

The big language model, or LLM, is composed of multiple neural network layers, namely the embedding layer, the loop layer, the feed-forward layer, and the attention layer. Each layer helps the model process and generate input. The embedding layer converts the input text into a high-dimensional vector representation, capturing semantic and syntactic information, and the model does not directly understand the original human text, and needs to be converted into a vector representation that the model can understand before entering the text into the model. The feed-forward network layer is a nonlinear transformation that helps the model learn higher-level abstractions in the input text. The loop layer translates the input text sequence in the form of a sequence, and each translation update the hidden state of that layer, which helps the model learn the dependency associations of different words in the sentence. The attention mechanism allows the model to focus on key parts of the input text, allowing it to more accurately predict the next output.

In general, LLM's architecture is to process the input text, capture the meaning of the text and the association between different words, and generate accurate predictions.

Examples: Here are some of the more popular large language models

• GPT-3: OpenAI is released with 175B parameters and handles tasks such as text generation, translation, and summary summaries.
• BERT: Released by Google, it has hundreds of M parameters, understands the contextual information of sentences, implements tasks such as question answering.
• XLNet: Released by Carnegie Mellon University and Google, mainly language generation and question answering.

• T5: Google released that T5 is trained in a large number of NLP tasks, handling tasks such as text translation, content summarization, and question answering.

• RoBERTa: Released by Facebook AI Lab, is an improved version of BERT.

Open source big language model

Researchers, developers, etc. can use open source big language models that have been released. Bloom is an open-source LLM trained on large-scale text data with 176B parameters, which is somewhat larger than GPT-3's parameters. Bloom can generate text content in 46 languages and program code in 13 languages, which can be used in a variety of application scenarios.

• Text generation: Generate text content in different styles, such as creative writing, writing code, scripting, etc.
• Translation: Translates a piece of text from one language to another.
• Q&A: You can answer questions on a variety of topics.

Bloom's architecture is very similar to GPT-3, but Bloom's training data includes a wider variety of languages. Bloom is a decoder-only architecture that includes an embedding layer and a multi-head attention layer. This architecture can be well adapted to training on different language data. Bloom can translate text, for example, into the English sentence "I love you" into the French sentence "Je t'aime." Another scenario is multilingual dialogue. Therefore, we can conclude that Bloom has a greater advantage in multilingual processing scenarios.

Hugging Face interface

First of all, we need to complete the registration on the official website of Hugging face and copy the token content needed for API access. Big language models are large-scale machine learning models that process and understand natural language through deep learning algorithms. Large language models are trained in a large-scale corpus to learn patterns and entity relationships in the language. Can handle a wide variety of language tasks such as text translation, sentiment analysis, chatbots, etc. Big language models can understand complex text data, identify entities and relationships between entities, and produce predictions that are in line with human habits.

Example 1: Sentence completion

The following code uses the hugging face API to make an API call, entered as a piece of text and some parameters.

Example 2: Q&A

summary

The big language model is a breakthrough in the field of natural language processing, bringing the technical level of text generation and text understanding to a new height. Large language models can learn from large-scale corpora, understand contexts and entities, and answer users' questions. This can replace many human jobs to some extent, and the big language model also creates new jobs in new directions and fields.