adminLANGUAGE MODELS
Language models mimic human ability to produce natural human language not only to produce but to converse, reason and make decisions. A language model uses a variety of statistical and probabilistic technique to predict the sequence of words based on the context and previous words.
Language models are employed in a variety of tasks which includes speech recognition and generation, language translation, text generation and recognition, text summarization etc.
There are several categories of language models. The main category is according to the size of the models. Thus language models are categorized as small, medium and large language model.
Small language models are models which are compact and efficient. They are designed or used for specific tasks or applications where computational resources are limited.
Medium language models fall in between small and large language models in terms of size and complexity. They are typically used for tasks that require a balance between computational efficiency and understanding of language and context.
Large language models are models with many parameters that capture complex patterns or relationship in language and are used for task that require a deep understanding of language and context.
The advantages of language models are as follows; language models can understand the nuances of language and their context when properly trained, enabling better and faster language processing.
Language models can generate coherent and natural sounding text making it useful for a wide range of applications. Language models can be used to improve machine translation systems thereby enabling a faster and more accurate language translation.
The disadvantages of language models are as follows; language models can perpetuate biases inherent in the training data resulting in unexpected errors and inaccurate outcomes. Language models may not have domain specific knowledge base thereby limiting their response and the ability to generate accurate or relevant text. Language models are only as good as the data they are trained on or fed thus poor quality data can lead to gross errors and inaccurate results.
Language models find widespread applications as chat bots, virtual assistant, language translation engines or machine translation systems, text content generation and summarization and a host of other applications.
The future of language models will depend on the advances in the following technologies; advances in language model architecture such as multimodal processing and transfer learning. Future language models will be trained on domain specific data enabling more accurate and relevant text generation in specialized field of knowledge. Future language models will be designed with inbuilt mechanisms to detect and mitigate all forms of bias. Future language models will incorporate artificial intelligence and other sophisticated tools to allow for seamless conversation artificial intelligence (AI) and human artificial intelligence (AI) collaboration.
SOURCES:
- Foundation models for natural language processing: pretrained 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.
