Why Is GPT Better Than BERT? A Detailed Review of Transformer Architectures

The field of natural language processing (NLP) has seen a significant shift since the introduction of transformer architectures. First proposed in the "Attention is All You Need" paper [5], these architectures have formed the basis for a variety of powerful models, including BERT and GPT.

In this article, we will overview transformer architectures. We will dissect the encoder-only architecture of BERT, exploring its masked language modeling and next-sentence prediction tasks. Next, we cover the decoder-only architecture of GPT models, highlighting its use of causal self-attention and scalability, we will also discuss why this architecture became dominant for general-purpose language models. Additionally, we will revisit the original Transformer architecture, which consists of both an encoder and a decoder.

BERT - Bidirectional Encoder Representations from Transformers [1]

Overview

BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained transformer-based model developed by researchers at Google AI Language. BERT revolutionized the NLP landscape by introducing a powerful encoder-only architecture that leverages bidirectional context for a wide range of tasks. The primary innovation of BERT lies in its ability to generate context-specific word embeddings, allowing it to capture complex language patterns and dependencies. This understanding of context has several important implications, which we will explore in more detail below.

Training details

The BERT model is trained on two tasks: Masked Language Modeling, where the objective is to predict the masked word based on the context (Fig. 1), and Next Sentence Prediction, where the objective is to classify whether sentence B follows Sentence A in the text.

Context-Dependent Word Embeddings

Traditional word embedding techniques, such as Word2Vec and GloVe, represent words as fixed-dimensional vectors that capture their general meaning. However, these embeddings do not account for the context in which words appear, which can limit their effectiveness in the modeling of language. BERT addresses this limitation by generating word embeddings that depend on the context in which the words are used. This enables BERT to capture the nuances of language and understand words with multiple meanings based on their surrounding tokens.

Understanding word in the context -> Understanding the context

One key insight of BERT is that the ability to generate context-dependent word embedding implies that the model understands the context itself. By learning to predict masked tokens based on their surrounding words, BERT demonstrates its ability to infer word meanings from context. This suggests that the model has developed a strong grasp of the language structure and relationships between words, which is essential for effective transfer learning.

Transfer Learning and Task Adaptation

BERT's understanding of context makes it particularly powerful for transfer learning, as its pre-trained representations can be easily fine-tuned for various downstream tasks. This adaptability is a key strength of BERT, as it allows the model to achieve state-of-the-art performance on a wide range of NLP tasks with minimal architectural changes and relatively small amounts of task-specific data.

Examples of tasks for which BERT has been successfully fine-tuned include:

• Sentiment analysis: BERT can be used to classify text based on sentiment, such as determining whether a movie review is positive or negative.

  
  

• Named entity recognition: Fine-tuning BERT can enable it to identify and classify entities, such as people, organizations, and locations, in a given text.

  
  

Summary

In summary, BERT's encoder-only architecture, context-dependent word embeddings, and deep understanding of context have made it a powerful and versatile model for transfer learning. Its success in a wide variety of NLP tasks has demonstrated the potential of transformer-based models and paved the way for subsequent developments in the field.

GPT - Generative Pre-trained Transformer

Overview

GPT (Generative Pre-trained Transformer) is a state-of-the-art transformer-based model developed by OpenAI. The third iteration in the GPT series, GPT-3 builds upon the success of its predecessors by scaling up the architecture and leveraging a massive amount of pre-training data. In contrast to BERT, GPT is based on the decoder-only Transformer architecture (Fig. 2). This section will explore the core insights of GPT, and highlight its important features that contribute to its success while drawing parallels with BERT.

Language Understanding through Next-Word Prediction

While BERT learns context-specific word embeddings through its masked language modeling task, GPT-3 focuses on predicting the next word in a given sequence, given the preceding words. Similar to BERT, this word prediction task allows GPT to develop a deep understanding of the language, as it must learn syntactic, semantic, and pragmatic aspects of the text in order to accurately predict what comes next.

Efficient Training with Causal Self-Attention

The first thing that contributes to the success of GPT models is the efficiency of the training process. GPT employs causal self-attention, a mechanism that allows tokens to only attend to preceding tokens during training. This design choice ensures that the model does not have access to future tokens when predicting the next word, making the training process more efficient. By processing a single text sample of length n, GPT effectively generates n training samples, as it predicts each token based on its preceding context. This increased efficiency in training enables GPT to scale up to larger architectures and datasets, resulting in improved performance and language understanding. In contrast, BERT uses masked language modeling, which involves masking a fixed subset of words and predicting them based on bidirectional context, requiring more computational resources during training.

Zero-Shot and Few-Shot Learning

The second thing that makes GPT models so interesting is the emergent ability to perform Zero-/ few-shot learning, i.e., by simply providing the model with a task description and zero or few examples of the desired output, GPT-3 can adapt to a wide range of NLP tasks without any task-specific fine-tuning (left image on Fig. 3). GPT-3 develops this remarkable capability only when it gets large enough, i.e., 100Bn+ parameters (right image on Fig.3). It’s possible to train GPT models of this size only due to the training efficiency described in Sec. 3.3, thus, it’s unlikely that BERT-like models will ever demonstrate few-shot learning capabilities and they would always require fine-tuning in practice.

The transfer learning capability of GPT-3 is a testament to the model's robust language understanding and its ability to generalize from the vast amount of pre-training data.

Conclusion

In conclusion, GPT-3's success in the field of natural language processing can be attributed to its efficient training based on the next word prediction, and zero-shot/few-shot learning capabilities. Its scalability allows it to handle larger architectures and datasets, enhancing its performance and language understanding.

Unlike BERT which requires task-specific fine-tuning and more computational resources during training, GPT-3 can adapt to various NLP tasks with minimal task-specific adjustments, highlighting its capacity for generalization from pre-training data.

Encoder-Decoder Architecture in Transformers

For completeness here we want to overview the original encoder-decoder architecture (Fig. 4) proposed in the seminal "Attention is All You Need" paper [5]. This architecture is used in practice for a wide range of NLP tasks. We already covered the encoder and decoder architectures, the encoder-decoder structure blends those two parts: the encoder processes the input sequence, and the decoder generates the output sequence, leveraging the representations from the encoder.

Here we want to highlight a significant difference between encoder-decoder Transformer models with BERT and GPT. Encoder-decoder models are typically specialized for specific tasks, such as translation or text summarization. This means a single model is typically trained to perform one specific function. On the other hand, BERT and GPT are designed to perform a wide array of tasks, and they excel at transfer learning. This difference underscores the versatility of BERT and GPT, while also highlighting the focused strength of task-specific encoder-decoder models.

References:

[1] 1810.04805.pdf (arxiv.org)

[2] The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning) – Jay Alammar – Visualizing machine learning one concept at a time. (jalammar.github.io)

[3] Transformer Decoder-Only 模型批量生成 Trick - 知乎 (zhihu.com)

[4] 2005.14165.pdf (arxiv.org)

[5] 1706.03762.pdf (arxiv.org)