2.3 Key Models: BERT, GPT, and T5

In the field of Large Language Models (LLMs), there are several prominent models, each specialized in different natural language processing (NLP) tasks. Notably, BERT, GPT, and T5 are models that represent the evolution of LLMs, each contributing to language understanding and generation with unique approaches. This section explores the differences and use cases of these models.

In the previous section, "Attention Mechanism: Self-Attention and Multi-Head Attention", we explained the self-attention mechanism and multi-head attention in the transformer model. Here, we delve into the key models BERT, GPT, and T5, which are built upon these attention mechanisms, highlighting their features and use cases.

BERT (Bidirectional Encoder Representations from Transformers)

BERT is an LLM developed by Google, known for its ability to understand context in both directions. Traditional models used a "unidirectional" approach, capturing context only from the past. In contrast, BERT uses a "bidirectional" model, simultaneously understanding the context before and after a word. This allows for a deeper understanding of the context, leading to high-precision results.

• Main Applications: Question answering, sentiment analysis, sentence classification
• Features: Bidirectional context understanding
• Example: A pre-training task where specific words in a sentence are masked, and the model predicts the masked words (Masked Language Model)

GPT (Generative Pre-trained Transformer)

The GPT series, developed by OpenAI, is an LLM primarily focused on text generation. GPT models use a "unidirectional" approach, considering only the context from the past. When given a part of a sentence, GPT can naturally generate the continuation. Notably, GPT-3, with its 175 billion parameters, is capable of generating complex sentences and engaging in dialogue.

• Main Applications: Text generation, chatbots, translation, creative writing
• Features: Unidirectional context, large number of parameters
• Example: Generating a long story or poem based on a user-provided prompt

T5 (Text-to-Text Transfer Transformer)

T5 is an LLM proposed by Google, characterized by treating all NLP tasks as "text-to-text" problems. T5 adopts a unified approach where both the input and output are text, allowing it to handle a wide variety of tasks consistently. This makes T5 versatile for tasks like question answering, translation, and summarization.

• Main Applications: Translation, summarization, question answering, document generation
• Features: Consistent framework treating all tasks as text transformation
• Example: Translating an English sentence into Japanese using the same model

These models are each optimized for different NLP tasks, making it important to choose the right model based on the project’s goals. BERT excels in high-precision context understanding, GPT is powerful in natural text generation, and T5 offers flexibility across a range of tasks. For engineers, selecting the right model is a crucial factor in project success.

In the next section, "LLM Training: Data Preprocessing and Fine-Tuning", we will cover methods for data preprocessing and fine-tuning to effectively utilize these models, helping you achieve optimal performance for specific tasks.