Democratizing NLP through GenAI - Exploring Generative AI's Impact on NLP

Generative AI (GenAI) and advanced Natural Language Technologies (NLT) are transforming the way we interact with machines, they are moving us from a world of "technology-literate people" to "people-literate technology".

This means that instead of users having to learn technology to operate machines, it’s the machines that learn and develop the intelligence to understand human requests spoken in natural language. In other words, instead of people learning technology to use the technology, it is technology learning to interact with humans.

instead of people learning technology to use the technology, it is technology learning to interact with humans.

This shift not only simplifies the user interface but enables people to communicate with technology products in a natural spoken way and eliminates the need for users to learn or to adapt to technology to use it effectively. This evolution is evident when we interact with today’s Large Language Models (LLM) such as GPT, LaMDA, Claude, Cohere Command, LLaMA, and others.

GenAI plays a crucial role in moving towards "people-literate technology".

What is Generative AI?

Generative Artificial Intelligence (GenAI) represents a groundbreaking branch of Artificial Intelligence (AI). It excels in creating new content—ranging from text to images, videos, music, and more—based on specific prompts or inputs. This capability marks a substantial advancement in the AI sector, making GenAI the “most innovative technology of this decade”. GenAI has the potential to enhance the existing use cases and also to develop entirely new ones.

What is Natural Language Processing?

Natural Language Processing (NLP) essentially gives computers the ability to comprehend human language, akin to teaching them how to converse with us. NLP enables computers to read, write, and converse, thereby opening vast possibilities that make our interactions with machines more intuitive and efficient. A few of the NLP Tasks are:

• Text Generation: Generating new text based on given input or prompts.
• Language Translation: Translating text from one language to another.
• Text Summarization: Generating short, coherent summaries of longer text documents while retaining the key information.
• Question Answering: Generating answers to questions posed in natural language.
• Sentiment Analysis: Identifying emotional tones in texts, such as positive or negative sentiments.
• Text Mining: Extracting insights and patterns from large volumes of text.
• Text Classification: Assigning categories or labels to text based on their content.
• Named Entity Recognition (NER): Identifying and classifying entities mentioned in text into predefined categories such as names of persons, organizations, locations, dates, etc.

Now Let’s focus on Generative AI’s Role in Natural Language Processing

GenAI's impact on NLP

Traditional NLP Approach

In traditional NLP, each specific task required its dedicated model, developed and fine-tuned for a particular purpose. For instance, a model trained for text generation excels in creating coherent and contextually relevant texts but would not be suitable for sentiment analysis. Similarly, Sentiment analysis models are designed for sentiment classification that cannot be used for Q&A.

Each of these models needs to be built from scratch, with separate, task-specific training data. The model optimized for one task cannot be repurposed for another without substantial modifications and retraining, which adds complexity and cost to NLP projects. As a result, the process is not only costly but also lacks flexibility, time-consuming, making it challenging to scale across different NLP applications.

One model, many tasks

Unlike traditional NLP approaches that require a distinct model for each task, GenAI models like GPT allow a single model to perform a variety of NLP tasks—from translation and summarization to question-answering and sentiment analysis—without needing task-specific training. This unified approach not only simplifies the development process but also reduces the time and resources needed to deploy NLP applications.

The below image illustrates the difference between traditional NLP and GenAI (Large Language Model or Foundational Model).

The secret sauce of one GenAI model performing multiple tasks is “emergent abilities”.

But how does a single GenAI model tackle various tasks at once?

The secret sauce of one GenAI model performing multiple tasks is “emergent abilities”.

Most GenAI models like GPT are autoregressive models, primarily trained “to predict the next word in a sentence, given all the previous words”. Interestingly, these models are not explicitly trained for certain NLP tasks such as NER, sentiment analysis, and more, but excel at them through "emergent abilities."

As these models are trained on vast datasets and scaled up to larger sizes, once they reach critical scales they exhibit many new capabilities. These new capabilities are called “emergent abilities”, and this enables a single model to perform a range of NLP tasks, showcasing the versatile and transformative power of generative AI.

These “emergent abilities” enable a single LLM model (like GPT) to perform a variety of NLP tasks like Text Generation, NER, chatbot, Questing and Answering, Sentiment Analysis, and much more.

To learn more about Emergent Abilities, read research paper: "Emergent Abilities of Large Language Models" - https://arxiv.org/abs/2206.07682. This paper is authored by Google Research, Stanford University, UNC Chapel Hill and DeepMind

"Emergent abilities” enable a single LLM model (like GPT) to perform a variety of NLP tasks like Text Generation, NER, QnA, Sentiment Analysis, and much more.

Democratizing NLP through GenAI

GenAI is not only reshaping the field of NLP; it is expanding its possibilities. GenAI is democratizing access to cutting-edge NLP technologies and making advanced NLP technologies widely accessible. With pre-trained models readily available, even regular Devs with minimal NLP expertise can implement sophisticated applications or build PoC. This eliminates the need for organizations to require an ML specialist or NLP specialist to build intelligent apps. For organizations, GenAI not only accelerates the Go-To-Market (GTM) but also significantly lowers entry barriers, facilitating rapid development at a lower cost.

For organizations, GenAI not only accelerates the Go-To-Market (GTM) but also significantly lowers entry barriers, enables accelerated development at a lower cost.