It is high time for OpenAI to put more focus on improving GPT-3 with pros of small language models

OpenAI has gained huge popularity after the launch of the artificial intelligence and NLP language model known as GPT-3. GPT-3 is flourishing in the global tech market for its performance in a wide variety of NLP tasks such as writing letters, composing articles like essays, stories, tweets, and many more. But OpenAI has missed some of the crucial points that are present in some small language models. GPT-3 has multiple pros and the group of users can reap the benefits of the NLP model. Let's explore some of the differences that make OpenAI GPT-3 model different from multiple other small language models. What is it that the popular tech company got missed while creating the artificial intelligence language model.

Features of OpenAI GPT-3

GPT-3 is an advanced artificial intelligence language model that can seamlessly write poems, press releases, technical manuals, and many more with better grammar sense. OpenAI also worked on the language model so that it can imitate different styles of different authors while writing code and composing music. It can also reply efficiently and effectively to different questions with basic comprehension as well as translate languages to make users understand.

The purpose of GPT-3 is to consume enormous volumes of text for predicting the next word to come next. OpenAI leveraged artificial neural networks as a logical architecture for machines to learn the maximum from large datasets. It consists of 175 billion training parameters on 45 TB of text sources from all sources on the internet, unlike small language models.

GPT-3 v/s small language models

Small language models are also thriving in the global tech market with few-shot performance. Small-language models are known as the few-shot learners helping researchers and practitioners in using those. Small language models can be much greener or eco-friendly than the large artificial intelligence models such as OpenAI GPT-3. This can be achieved by converting textual inputs into close questions while consisting of the task description the combination with gradient-based optimization. There are other key factors that are required for successful NLP understanding with multiple small language models.

Small language models can require unlabelled data for the distillation of the knowledge of all kinds of models, unlike GPT-3. The unlabelled data is known for generating training sets for future generations. GPT-3 holds the potential to be misused, unlike these small language models. OpenAI discovered that this artificial intelligence NLP model consists of racial, religious, as well as gender bias. GPT-3 can show discrimination and structural inequalities to pose a danger to the world with societal bias.

Thus, OpenAI GPT-3 holds the potential to enhance the efficiency of workflows and other deliverables while empowering human beings in new different ways in the global tech market. Thus, the tech company can put more focus on the artificial intelligence and NLP model to work better than small language models in the nearby future.