What is NLP?
Natural Language Processing (NLP) is a field of computer science and artificial intelligence that focuses on enabling computers to understand and process human language. It is used to analyze, understand, and generate human language data, and has a wide range of applications across various industries. This technology has been around for many years, but recent developments in the field have drastically changed what is possible with this technology.
One of the primary challenges of NLP is the complexity of human language. Unlike programming languages, which have clear rules and syntax, human language is highly ambiguous and context-dependent. For example, the same word can have different meanings depending on the context in which it is used, and multiple words can be used to express the same idea. This complexity can make it difficult for computers to accurately understand and interpret human language data.
Despite these challenges, in the last few years, NLP has become increasingly important in various industries: In customer service, NLP has been used for years to create virtual assistants that can respond to customer inquiries and provide assistance or automatically classify and reroute emails. In marketing, NLP is well recognized tool used to analyze social media data and identify the main topics and the sentiment associated to social media posts; and then identify trends and patterns that can inform marketing strategies, or to create marketing plans or contents.
Today, with the recent development of foundational models, such as GPT4 and ChatGPT, NLP is indisputably one of the technologies with the most transformative potential that we have seen in years. It will change the world as we know it. But where do these foundational models stand in the field of NLP and what differentiates them from previous NLP algorithms?
Types of NLP Models
There are three main types of NLP models: rule-based systems, statistical models, and deep learning models.
Rule-based systems are the oldest type of NLP models. They rely on a set of predefined rules that are used to analyze and process language data. For example, a rule-based system might be programmed to identify the parts of speech in a sentence based on a set of grammatical rules.
Statistical models are a more recent development in NLP, and they work by analyzing large datasets to identify patterns and trends in language data. These models use statistical algorithms to determine the likelihood that a given word or phrase is associated with a particular meaning or context.
Deep learning models are the most advanced type of NLP models, and they are based on neural networks that are designed to simulate the way the human brain processes information.
Foundational models such as BERT, GPT-3 and ChatGPT belong to the family of deep learning models and are a recent breakthrough in the field of NLP. They work by training on large datasets of text, and they can generate human-like language that is difficult to distinguish from text written by a human. These models have changed the possibilities offered by NLP technology, enabling businesses to automate complex language tasks, such as writing product descriptions or generating responses to customer inquiries with results virtually indistinguishable from what a human could do.
The impact of foundational models
BERT, GPT are examples of foundational models that have revolutionized NLP applications. They have made it possible to build NLP applications that can perform various language-related tasks much more accurately and efficiently than their predecessors .
BERT (Bidirectional Encoder Representations from Transformers) is based on transformers, a family of deep learning models in which every output element is connected to every input element and the weightings between them are dynamically calculated based upon their connection. Transformers is considered a significant improvement to previous models because it doesn't require sequences of data to be processed in any fixed order. Because Transformers can process data in any order, they enable training on larger amounts of data than ever was possible before their existence.
With the help of BERT, several NLP tasks such as sentiment analysis, text classification, or named entity recognition (NER), can be performed with a higher degree of accuracy than what was previously possible.
Even with the additional capabilities offered by transformer models, language generation, one of the most complex NLP tasks, remained unconvincing with model such as BERT and was considered non mature until the revolution brought by the family of OpenAI models called GPT, and in particular, GPT3, which is at the heart of the model powering ChatGPT today.
With GPT3, OpenAi has revolutionized language generation. It has absorbed an enormous amount of information found on the internet, in virtually every field of knowledge, and can generate text that is almost indistinguishable from human-written text. This has significant implications for applications such as chatbots, content generation, and language translation.
With GPT3, AI can generate responses that are more human-like, while content generation systems can create articles and reports that are engaging and informative. But GPT3 can also be used as a virtual assistant helping people get access to highly specialised information in a quick and easy way.
The impact of these foundational models is even bigger: they have also reduced the need for extensive labeled data, which was previously required to train NLP models. NLP applications are easier and faster to build and the entry cost of building an application is greatly reduced. With pre-trained models like BERT and GPT, developers can use the models as a starting point and fine-tune them for specific tasks, rather than building models from scratch.
With foundational models, businesses can now process vast amounts of language data more efficiently than ever before, and become more efficient in tasks that previously had to be done by humans.
What’s the catch?
However, as with any new technology, there are concerns and challenges that must be addressed. One of the major concerns with foundational models is the potential for these models to “hallucinate”, or generate content that is factually incorrect or inappropriate. These hallucinations happen because these models generate content that is statistically relevant, but do not have a sense of reasoning or understanding of the answers they generate, which can lead to the generation of false or misleading information.
To address this concern, humans absolutely need to be integrated in the loop and have enough knowledge on the proposed subjects to ensure that the generated content is of high quality and accurate.
Despite these concerns, the NLP applications that are now possible with these models are immense. Furthermore, these models continue to evolve, as GPT4, the latest version OpenAI models, juste proved: This new version is indeed capable of successfully passing high level university exams with high grades, in various fields. With the foundational models, the language treatment capabilities of AI have reached a new high, which will probably revolutionise our world in the near future.
If you found this article insightful, be sure to stay tuned for the upcoming parts of our blog series. These will provide more information on how companies can utilize these models, and will be available in the next posts of this series on foundational models.
