SMALL LANGUAGE MODELS

Small language models are compact, highly efficient and are used where limited resources are available. They are smaller, faster and are more easily customized than medium or large language models.

Small language models have fewer parameters than the other models; typically their parameter size is less than 100million, which dramatically reduces the computational cost and energy usage. They are focused on specific tasks and are trained on smaller datasets. However the key characteristics of small language models are their efficiency, accessibility, customization and faster inference or quick response time. In fact small language models are light weight versions of large language models.

There are several types of small language models and they are; knowledge distillation, pruning and quantization models.

Knowledge distillation models involve training a smaller model using knowledge from a larger model.

Pruning model involves removing redundant or less important parameters within the neural network architecture.

Quantization models involve reducing the precision of numerical values used in calculations such as converting floating numbers to integers.

The advantages of small language models are as follows; small language models require less computational resources making them suitable for application where resources are limited. Small language models can process text quickly enabling fast response times. Small language models can be deployed on edge devices to enable offline processing and reducing reliance on cloud infrastructure.

The disadvantages of small language models are as follows; small language models accuracy is limited particularly on complex tasks or datasets. Small language models may not capture the same level of contextual understanding as large language models, which may lead to potentially less coherent or relevant text generation. Small language models may not have same level of domain specific knowledge as larger models thereby limiting their ability to generate accurate or relevant text.

The application of small language models are widespread and are used in a variety of devices such as mobile phones, edge devices such as smart speakers and smart home devices to enable offline processing and reduce reliance on cloud infrastructure. Small language models can be used in applications that require real time processing such as live chat bots or virtual assistants.

The future of small language model is based on the advances in the following technologies. Future small language models may incorporate new models with compression techniques such as pruning, quantization and knowledge distillation hybrid models to further reduce their size and computational requirements. Small language models may become popular and trending such that more small language model devices will be added to the network thereby reducing dependence on the cloud infrastructure for off line processing. Future small language models may achieve better accuracy and contextual understanding through the use of new small language architectures or training techniques.

SOURCES:

• DistilBert : a distilled version of BERT by Victor Sanh, Lysandre Debut, Julien Chaumond and Thomas Wolf.
• Distilling the knowledge in a neural network by Geoffrey Hinton, Oriol Dinyals and Jeff Dean.
• Natural language processing (almost) from scrtch by Collobert et al.
• Deep learning by Ian Goodfellow, Yoshua Benigo and Aaron Covrilte.
• Speeh and language processing by Dan Jurafsky and James H. Martin.