LARGE LANGUAGE MODELS - Prompt engineering
Large language models refer to advanced neural network-based architectures that have been
trained on massive amounts of text data to process and understand human language. These
models have shown remarkable capabilities in various natural language processing (NLP) tasks,
such as language generation, translation, question-answering, sentiment analysis, and more.
They are typically characterized by having tens of billions of parameters, allowing them to
capture complex language patterns and generate coherent and contextually relevant responses.
Some of the well-known large language models include:
GPT-3 (Generative Pre-trained Transformer 3): Developed by OpenAI, GPT-3 is one of the most
famous and largest language models, with 175 billion parameters. It has demonstrated
impressive performance across a wide range of NLP tasks and can generate human-like text.
BERT (Bidirectional Encoder Representations from Transformers): Developed by Google, BERT
is another influential language model with 340 million parameters. It introduced the concept of
bidirectional training and context-based word embeddings, leading to significant improvements
in many NLP tasks.
T5 (Text-to-Text Transfer Transformer): Developed by Google, T5 is a large model that frames
all NLP tasks as a text-to-text problem. It has 11 billion parameters and has shown strong
performance in a multitude of NLP tasks.
XLNet: Developed by Google, XLNet is a generalized autoregressive pre-training method that
leverages both autoregressive and autoencoding objectives. It has 340 million parameters and
has achieved state-of-the-art results in various NLP benchmarks.
RoBERTa (A Robustly Optimized BERT Pretraining Approach): A variation of BERT developed by
Facebook AI, RoBERTa uses a larger batch size and more training data to achieve better
performance across multiple NLP tasks.
Large language models refer to advanced neural network-based architectures that have been
trained on massive amounts of text data to process and understand human language. These
models have shown remarkable capabilities in various natural language processing (NLP) tasks,
such as language generation, translation, question-answering, sentiment analysis, and more.
They are typically characterized by having tens of billions of parameters, allowing them to
capture complex language patterns and generate coherent and contextually relevant responses.
Some of the well-known large language models include:
GPT-3 (Generative Pre-trained Transformer 3): Developed by OpenAI, GPT-3 is one of the most
famous and largest language models, with 175 billion parameters. It has demonstrated
impressive performance across a wide range of NLP tasks and can generate human-like text.
BERT (Bidirectional Encoder Representations from Transformers): Developed by Google, BERT
is another influential language model with 340 million parameters. It introduced the concept of
bidirectional training and context-based word embeddings, leading to significant improvements
in many NLP tasks.
T5 (Text-to-Text Transfer Transformer): Developed by Google, T5 is a large model that frames
all NLP tasks as a text-to-text problem. It has 11 billion parameters and has shown strong
performance in a multitude of NLP tasks.
XLNet: Developed by Google, XLNet is a generalized autoregressive pre-training method that
leverages both autoregressive and autoencoding objectives. It has 340 million parameters and
has achieved state-of-the-art results in various NLP benchmarks.
RoBERTa (A Robustly Optimized BERT Pretraining Approach): A variation of BERT developed by
Facebook AI, RoBERTa uses a larger batch size and more training data to achieve better
performance across multiple NLP tasks.
