LARGE LANGUAGE MODELS

Large language models (LLMs) are artificial intelligence models that are designed to understand and generate human language. These models are trained with vast amount of data to enable them to capture the complexities and nuances of human language. Large language models data size is estimated to range from 1billion to 10billion parameters or even more. The vast amount of data fed to large language models differentiate them from small and medium language models in terms pf their superior ability to generate a more coherent and contextually relevant text.

There are several types of large language models, the main ones are as follows; transformer-based, autoregressive, masked, sequence to sequence, retrieval-augmented, recurrent neural network and hybrid models.

Transformer-based models use the transformer architecture which relies on self-attention mechanisms to process input sequences.

Autoregressive models generate text one token at a time using the previous tokens as context.

Masked language models are trained to predict masked tokens in a sequence allowing them to learn bidirectional representations of language.

Sequence to sequence models consist of an encoder and a decoder where the encoder processes the input sequence and the decoder generates the output sequence.

Retrieval-augmented models combine the strengths of language models with retrieval mechanisms to access external knowledge source.

Recurrent neural network models use recurrent neural networks to process input sequences. Recurrent neural networks are particularly well suited for modeling sequential data such as text. Recurrent neural networks can be used as a type of autoregressive model where the model generates text one token at a time. However recurrent neural networks have limitations in their use such as vanishing gradients making it difficult to train deep models and they are slow to process long sequences of text.

Hybrid models combine different architectures or approaches to leverage their strengths. For example a model might combine transformer-based and autoregressive components.

The advantages of large language models are as follows; large language models can capture complex linguistic patterns and relationships enabling better language understanding. Large language models can generate coherent and contextually relevant text, making them useful for numerous applications such as content generation and chat bots. Large language models can be fine-tuned for specific tasks and domains making them adaptable to various applications.

The disadvantages of large language models are as follows; training and deploying large language models require significant computational resources and infrastructure. Large language models can perpetuate biases and unfairness present in the training data which can have negative consequences.

Large language models find widespread application and use in the following areas of human endeavors such as natural language translation, sentiment analysis, question answering, speech and hand writing recognition, text generation etc.

Large language models are now used to build chat bots and conversation artificial intelligence systems that understand and respond to users queries. Large language models are now used to generate content such as articles, social media posts, art, medical prescriptions etc.

The future of large language models will be driven by the following; increased adoption in the wider society and industries will spur tremendous growth in all technologies especially in research and development. Advances in computer and related technologies if create more efficient and cost effective large language models especially as may involve vision and speech etc.

 

SOURCES:

• Foundation models for natural language processing: pre-trained language models integrating media by Gerhard PaaB and Svern Giesselbach.
• Speech and language processing by Dan Jurafsky etal.
• Modern language models and computations: Theory with applications by Alexander Meduna and Ondrej Soukup.
• Large language models by Afshine Amidi and Sharinne Amidi.
• Build a large language model from scratch by Sebastian Raschka.