What's the difference in Natural Language Processing, Natural Language Understanding & Large Language Models?

Natural language processing (NLP) aims to give computers the same level of understanding of written and spoken language as humans. (Image via Google)

There is still knowledge available about large language models (LLMs) and ChatGPT, however, it is worth exploring Natural Language Processing (NLP), which is sweeping the Artificial Intelligence (AI) and tech fields right now. While ChatGPT and other LLMs have helped it grow, but what is  NLP?

NLP and its purpose

With natural language processing (NLP), computers can translate between languages, follow verbal commands, and quickly summarise vast amounts of text, sometimes in real-time. Voice-activated navigation systems, digital assistants, dictation programs, chatbots for customer support, and other modern conveniences use NLP. Today, NLP is widely used in corporate solutions to promote business efficiency and employee productivity and simplify critical business procedures.

What is the goal of NLP?

NLP aims to give computers the same level of understanding of written and spoken language as humans. The combination of statistical, Machine Learning, and deep learning models with computational linguistics is called NLP. These technologies allow computers to 'understand' human language in text or speech data, including the speaker or writer's intent and sentiment.

What is an LLM?

A large language model (LLM) is a type of language model that stands out because it can understand and create any language. LLMs learn these skills by using vast amounts of data to retain billions of parameters during training and using a lot of computing power while learning and running. Self-supervised and semi-supervised learning is used to train LLMs and artificial neural networks.

Is NLP the same as LLM?

LLM can do several NLP jobs. Large language models use transformer models and vast datasets to train. It lets them read, translate, forecast, and create content.

Tasks it serves

To comprehend, summarise, synthesise, and anticipate new material, large language models (LLMs) employ deep learning techniques and enormous data sets.

Where it's applicable

OpenAI's GPT series, Google's BERT, and Facebook's RoBERTa are all well-known LLMs. Chatbots, language translation programs, and content production programs have all used these models.

How does it work?

Large language models are capable of teaching AI applications to human languages. They can be trained to do various activities, such as deciphering protein structures and generating software code. Large language models need to be pre-trained and then fine-tuned to tackle challenges related to text categorisation, question answering, document summarising, and text generation, just like the human brain. Their ability to solve problems can be used in industries, including healthcare, banking, and entertainment, where large language models are used for a range of natural language processing (NLP) uses, including chatbots, AI assistants, translation, and more.

What is NLU?

Natural language interpretation (NLI) or natural language understanding (NLU) is a subfield of Artificial Intelligence's natural-language processing focusing on machine reading comprehension. Understanding natural language is regarded as an AI-hard problem. It understands our language with the help of extensive libraries of knowledge and sophisticated algorithms. Natural Language Understanding lets computers understand and react correctly to the feelings expressed in natural language text instead of relying on the syntax of computer language.

NLU bridges the gap between how people talk and how computers understand them. NLP breaks down language into a code that computers can read and process. NLU, on the other hand, understands what people are saying.

A Natural Language Understanding-based tool's primary objective is to provide an understandable response to the input provided by the user. Many businesses are interested in this area because it can be used for automated reasoning, machine translation, question answering, news gathering, text sorting, voice activation, archiving, and large-scale content analysis.

Some of the examples are:

• Automatic Ticket Routing
• Machine Translation (MT)
• Automated Reasoning
• Automatic Ticket Tagging & Reasoning
• Question Answering.

The natural language processing market is anticipated to increase at a CAGR of 30.22 per cent between 2022 and 2027, or $53,505.91 million. Increasing demand for NLP applications is a significant factor propelling market expansion. Electronic commerce platforms employ NLP to enhance the customer experience and boost revenue. Utilising sentiment analysis, chatbots, and personalised recommendations accomplishes this.

Additionally, organisations understand that NLP can increase consumer satisfaction and operational efficiency. Customer service virtual assistants and automated messaging systems are facilitated by NLP to guarantee individualised and efficient interactions with clients. These factors are, therefore, anticipated to stimulate market expansion throughout the forecast period.