Video 308: An introduction to language models, With a special focus on GPT
Language models are the foundation of many natural language processing (NLP) tasks.
They help machines understand and generate human language by predicting the likelihood of a sequence of words.
Over the years, advances in algorithms and computational power have driven progress in language modeling, enabling breakthroughs in NLP applications.
LSTM networks, introduced by Hochreiter and Schmidhuber in 1997, are a type of recurrent neural network (RNN) designed to handle long-term dependencies.
Traditional RNNs struggled with the vanishing gradient problem, making it difficult to capture context over longer sequences.
LSTMs addressed this issue with their unique gating mechanisms, which enabled them to retain information for more extended periods, paving the way for improved language modeling.
(Watch my video on this topic: • 167 - Text prediction ... )
The transformer architecture, introduced by Vaswani et al. in 2017, revolutionized NLP by utilizing self-attention mechanisms and parallel processing.
The Transformer model is based on the encoder-decoder architecture.
Encoder: Processes input sequence, generating contextualized representations of each token.
Decoder: Generates output sequence step by step, using encoder's output as context for informed predictions.
Self-attention allows the model to weigh the importance of different words in a sequence, enabling better context understanding.
Parallel processing overcomes the sequential processing limitations of RNNs, leading to faster training and improved performance on various NLP tasks.
BERT (Bidirectional Encoder Representations from Transformers) is well-suited for tasks that require understanding the context of both preceding and following tokens. Some good applications for BERT include:
Sentiment analysis
Named entity recognition
Question-answering systems
Text classification
Semantic role labeling
GPT (Generative Pre-trained Transformer) is primarily designed for text generation tasks, and it is a unidirectional model, meaning it processes text in a left-to-right fashion. Some good applications for GPT include:
Text completion
Machine translation
Summarization
Chatbots and conversational AI
Creative writing assistance
GPT, developed by OpenAI, is a transformer-based model with a focus on decoding and adaptability.
GPT models, particularly GPT-3, have demonstrated impressive capabilities in zero-shot and few-shot learning, where they can learn new tasks with minimal or no examples.
While GPT excels at text generation and learning from examples without fine-tuning, it is important to consider its limitations, such as the size and computational requirements of the model, when evaluating its practical applications.
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