Why BERT is Not GPT

🌈 Abstract

The article discusses the evolution of language models, focusing on the advancements in neural network architectures and the emergence of large pre-trained models like BERT and GPT. It covers the key concepts and techniques, including word embeddings, recurrent neural networks (RNNs), long short-term memory (LSTMs), and the attention mechanism that led to the development of transformers and large language models.

🙋 Q&A

[01] Word Embeddings and N-Grams

1. What is word embedding, and how does it capture semantic relationships between words?

• Word embedding is a technique in natural language processing (NLP) where words are represented as vectors in a continuous vector space.
• These vectors capture semantic meanings, allowing words with similar meanings to have similar representations. For example, the words "king" and "queen" would have vectors that are close to each other, reflecting their related meanings.
• A famous example of word embedding is Word2Vec, which uses n-grams to train on context windows of words and capture semantic relationships.

2. What are the two main approaches in Word2Vec?

• Continuous Bag of Words (CBOW): Predicts a target word based on its surrounding context (n-grams).
• Skip-gram: Predicts the surrounding words given a target word.

3. How do Word2Vec and n-grams facilitate various NLP tasks?

• Word2Vec uses context from large corpora to learn word associations, enabling it to provide a rich representation of words based on their usage patterns.
• This facilitates various NLP tasks, such as sentiment analysis and machine translation, by providing meaningful word embeddings.

[02] Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTMs)

1. What are the key characteristics of RNNs and LSTMs?

• RNNs are a type of neural network designed for sequential data, processing inputs sequentially and maintaining a hidden state to capture information about previous inputs.
• LSTMs are a specialized type of RNN designed to overcome the limitations of standard RNNs, particularly the vanishing gradient problem, using gates (input, output, and forget gates) to regulate the flow of information and maintain long-term dependencies.

2. How do RNNs and LSTMs differ from Word2Vec?

• Purpose: Word2Vec is primarily a word embedding technique, while RNNs and LSTMs are used for modeling and predicting sequences.
• Architecture: Word2Vec employs shallow, two-layer neural networks, while RNNs and LSTMs have more complex, deep architectures designed to handle sequential data.
• Output: Word2Vec outputs fixed-size vectors for words, while RNNs and LSTMs output sequences of vectors, suitable for tasks requiring context understanding over time.
• Memory Handling: LSTMs can effectively manage long-term dependencies due to their gating mechanisms, making them more powerful for complex sequence tasks.

[03] Attention Mechanism and Transformers

1. What is the attention mechanism, and how does it work?

• The attention mechanism is a key component in neural networks, particularly in transformers and large pre-trained language models, that allows the model to focus on specific parts of the input sequence when generating output.
• It assigns different weights to different words or tokens in the input, enabling the model to prioritize important information and handle long-range dependencies more effectively.

2. How do transformers utilize the attention mechanism?

• Transformers use self-attention mechanisms to process input sequences in parallel rather than sequentially, as done in RNNs.
• This allows transformers to capture contextual relationships between all tokens in a sequence simultaneously, improving the handling of long-term dependencies and reducing training time.
• The self-attention mechanism helps in identifying the relevance of each token to every other token within the input sequence, enhancing the model's ability to understand the context.

3. How do large pre-trained language models like BERT and GPT leverage the attention mechanism?

• Large pre-trained language models, such as BERT and GPT, are built on transformer architectures and leverage attention mechanisms to learn contextual embeddings from vast amounts of text data.
• These models utilize multiple layers of self-attention to capture intricate patterns and dependencies within the data, enabling them to perform a wide range of NLP tasks with high accuracy after fine-tuning on specific tasks.

[04] Comparing BERT and GPT

1. What are the key similarities between BERT and GPT?

• Both BERT and GPT are based on the transformer architecture and are considered large pre-trained language models.

2. What are the main differences between BERT and GPT?

• Directionality: BERT is bidirectional, while GPT is unidirectional.
• Primary Task: BERT is designed for language understanding tasks, such as question answering and language inference, while GPT is primarily generative, able to create new text content.
• Pre-training Approach: BERT is pre-trained using a masked language model objective, while GPT is pre-trained using a generative language modeling objective.