What is the LLM Big Language Model? Definition, training methods, reasons for popularity, and examples – AI encyclopedia knowledge

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In recent years, the field of artificial intelligence (AI) has experienced tremendous growth, and natural language processing (NLP) is one of the areas that has made rapid progress. The most important development in NLP is the Large Language Model (LLM), which may completely change the way we interact with technology. Coupled with the explosion of OpenAI’s GPT-3, the LLM has gained more attention in the industry. In this article, we will briefly introduce the big language model, popularize its definition, training methods, popularity reasons, common examples of big language models, and the challenges it faces.
Definition of the Big Language Model
Large Language Model (LLM), also known as Large Language Model, is an artificial intelligence model aimed at understanding and generating human language. They are trained on a large amount of text data and can perform a wide range of tasks, including text summarization, translation, sentiment analysis, and so on. The characteristic of LLM is its large scale, containing billions of parameters, helping them learn complex patterns in language data. These models are typically based on deep learning architectures, such as converters, which help them achieve impressive performance on various NLP tasks.
Training methods for large language models
Training a language model requires providing it with a large amount of textual data, which the model uses to learn the structure, syntax, and semantics of human language. This process is usually completed through unsupervised learning, using a technique called self supervised learning. In self supervised learning, the model generates its own label for the input data by predicting the next word or marker in the sequence, and provides the previous word.
The training process includes two main steps: pre training and fine-tuning:
In the pre training stage, the model learns from a large and diverse dataset, typically containing billions of words from different sources such as websites, books, and articles. This stage allows the model to learn general language patterns and representations.
In the fine-tuning stage, the model is further trained on more specific and smaller datasets related to the target task or domain. This helps the model to fine tune its understanding and adapt to the specific requirements of the task.
The reasons for the popularity of the big language model
The main reasons why big language models are becoming increasingly popular are as follows:
Performance improvement: The massive scale of large language models enables them to capture complex language patterns, demonstrating impressive abilities in various tasks, especially in terms of accuracy and fluency, often surpassing previous state-of-the-art methods.
Transfer learning: Large language models can be fine tuned for specific tasks, allowing the model to quickly adapt to new fields using its general language understanding. This transfer learning ability greatly reduces the need for specific task data and training time.
Multifunctionality: Large language models can perform multiple tasks without the need for specific task architectures or models, and can be used for text generation, translation, summarization, etc., making them highly flexible and versatile in various applications.
High interactivity: The ability of large language models to understand and generate human like responses enables them to interact more naturally and intuitively with artificial intelligence systems, providing new possibilities for AI driven tools and applications.
Common Big Language Models
GPT-3 (OpenAI): Generative Pre trained Transformer 3 (GPT-3) is one of the most famous LLMs with 175 billion parameters. This model has shown significant performance in text generation, translation, and other tasks, and has received enthusiastic feedback worldwide. Currently, OpenAI has iterated to the GPT-4 version.
BERT (Google): Bidirectional Encoder Representations from Transformers (BERT) is another popular LLM that has had a significant impact on NLP research. This model uses a bidirectional approach to capture context from both sides of a word, improving the performance of various tasks such as sentiment analysis and named entity recognition.
T5 (Google): Text to Text Converter (T5) is an LLM that limits all NLP tasks to text to text problems, simplifying the process of adapting the model to different tasks. T5 demonstrates strong performance in tasks such as summarizing, translating, and answering questions.
ERNIE 3.0 Wenxin Big Model (Baidu): The big language model ERNIE 3.0 launched by Baidu introduces large-scale knowledge graphs for the first time in pre training models of billions and billions, proposing a parallel pre training method for massive unsupervised text and large-scale knowledge graphs.
The Challenges Faced by Large Language Models
Although the ability of large language models is impressive, they still face some challenges:
Huge resource consumption: Training LLM requires a large amount of computing resources, which poses challenges for smaller organizations or researchers in developing and deploying these models. In addition, energy consumption related to training LLM has also caused certain environmental problems.
Output may be biased: Due to the bias in the training data, LLM can learn and continue the bias in its training data, resulting in biased outputs that may be offensive, discriminatory, or even erroneous ideas.
Limited understanding ability: Although large language models have the ability to produce seemingly coherent and contextual text, LLM sometimes lacks a profound understanding of the concepts they write, which can lead to incorrect or meaningless output.

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