Study Card: Key Differences between GPT and BERT

Direct Answer

GPT and BERT are both transformer-based language models but differ significantly in their architecture and training objectives. GPT is an autoregressive model, trained to predict the next word in a sequence, making it suitable for text generation tasks. BERT, on the other hand, is a bidirectional model trained using masked language modeling and next sentence prediction, excelling at understanding context and relationships within text, making it ideal for tasks like text classification and question answering. Key differences include unidirectional vs. bidirectional processing, text generation vs. context understanding, and different pre-training tasks.

Key Terms

• Autoregressive Model: A model that predicts the next item in a sequence based on the preceding items (e.g., GPT).
• Bidirectional Model: A model that considers both preceding and following items in a sequence to understand the context of an item (e.g., BERT).
• Masked Language Modeling (MLM): A training technique where some words in a sentence are masked, and the model is trained to predict the masked words based on the surrounding context.
• Next Sentence Prediction (NSP): A training technique where the model is given two sentences and trained to predict whether the second sentence follows the first sentence in the original text.
• Transformer: A neural network architecture based on self-attention mechanisms, allowing the model to weigh the importance of different parts of the input sequence.

Example

Given the sentence "The quick brown fox jumps over the lazy dog.", GPT would be trained to predict "jumps" given "The quick brown fox," "over" given "The quick brown fox jumps," and so on. BERT, using MLM, might be given "The quick brown [MASK] jumps over the [MASK] dog" and trained to predict "fox" and "lazy". For NSP, BERT would be given pairs of sentences and determine if they are consecutive in the original text.

Code Implementation

# Demonstrates usage of pre-trained GPT-2 and BERT models for different tasks

from transformers import pipeline

# Text Generation with GPT-2
generator = pipeline('text-generation', model='gpt2')
generated_text = generator("The quick brown fox jumps over the", max_length=30, num_return_sequences=1)
print("GPT-2 Generated Text:", generated_text[0]['generated_text'])

# Masked Language Modeling with BERT
unmasker = pipeline('fill-mask', model='bert-base-uncased')
masked_text = "The quick brown [MASK] jumps over the lazy dog."
filled_text = unmasker(masked_text)
print("BERT Filled Text:", filled_text[0]['sequence'])

# Note: For Next Sentence Prediction (NSP) with BERT, you'd typically
# use a specific BERT model configuration and manually extract the
# relevant output from the model. Simplified example below:
from transformers import BertTokenizer, BertForNextSentencePrediction
import torch

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForNextSentencePrediction.from_pretrained('bert-base-uncased')

prompt = "The quick brown fox jumps over the lazy dog. The dog barked."
encoded = tokenizer.encode_plus(prompt, return_tensors='pt')

with torch.no_grad():
    outputs = model(**encoded)
    seq_relationship_logits = outputs.seq_relationship_logits
    is_next = seq_relationship_logits.argmax().item()
    print(f"Are the sentences consecutive according to BERT? {'Yes' if is_next == 0 else 'No'}")

Related Concepts

• Transfer Learning: Both GPT and BERT utilize transfer learning. Interviewers might ask about the benefits and limitations of transfer learning in NLP.
• Attention Mechanisms: The transformer architecture uses self-attention. Understanding the attention mechanism and its role in these models is crucial.
• Pre-training Objectives: The different pre-training objectives (autoregressive vs. MLM/NSP) lead to different strengths. Interviewers might ask about designing pre-training tasks for specific applications.
• Fine-tuning: Both models are typically fine-tuned for downstream tasks. Interviewers might ask about fine-tuning strategies and how to avoid overfitting.
• Model Architectures: While both are based on transformers, variations exist. Understanding these architectural differences can be beneficial.