25 of the best large language models in 2025

What Is NLP? Natural Language Processing Explained

Natural language processing definition

Natural language processing (NLP) is the branch of artificial intelligence (AI) that deals with training computers to understand, process, and generate language. Search engines, machine translation services, and voice assistants are all

While the term originally referred to a system's ability to read, it's since become a colloquialism for all computational linguistics. Subcategories include natural language generation (NLG) — a computer's ability to create communication of its own — and natural language understanding (NLU) — the ability to understand slang, mispronunciations, misspellings, and other variants in language.

The introduction of transformer models in the 2017 paper "Attention Is All You Need" by Google researchers revolutionized NLP, leading to the creation of generative AI models such as Bidirectional Encoder Representations from Transformer (BERT) and subsequent DistilBERT — a smaller, faster, and more efficient BERT — Generative Pre-trained Transformer (GPT), and Google Bard.

How natural language processing works

NLP leverages machine learning (ML) algorithms trained on unstructured data, typically text, to analyze how elements of human language are structured together to impart meaning. Phrases, sentences, and sometimes entire books are fed into ML engines where they're processed using grammatical rules, people's real-life linguistic habits, and the like. An NLP algorithm uses this data to find patterns and extrapolate what comes next. For example, a translation algorithm that recognizes that, in French, "I'm going to the park" is "Je vais au parc" will learn to predict that "I'm going to the store" also begins with "Je vais au." All the algorithm then needs is the word for "store" to complete the translation task.

NLP applications

Machine translation is a powerful NLP application, but search is the most used. Every time you look something up in Google or Bing, you're helping to train the system. When you click on a search result, the system interprets it as confirmation that the results it has found are correct and uses this information to improve search results in the future.

Chatbots work the same way. They integrate with Slack, Microsoft Messenger, and other chat programs where they read the language you use, then turn on when you type in a trigger phrase. Voice assistants such as Siri and Alexa also kick into gear when they hear phrases like "Hey, Alexa." That's why critics say these programs are always listening; if they weren't, they'd never know when you need them. Unless you turn an app on manually, NLP programs must operate in the background, waiting for that phrase.

Transformer models take applications such as language translation and chatbots to a new level. Innovations such as the self-attention mechanism and multi-head attention enable these models to better weigh the importance of various parts of the input, and to process those parts in parallel rather than sequentially.

Rajeswaran V, senior director at Capgemini, notes that Open AI's GPT-3 model has mastered language without using any labeled data. By relying on morphology — the study of words, how they are formed, and their relationship to other words in the same language — GPT-3 can perform language translation much better than existing state-of-the-art models, he says.

NLP systems that rely on transformer models are especially strong at NLG.

Natural language processing examples

Data comes in many forms, but the largest untapped pool of data consists of text — and unstructured text in particular. Patents, product specifications, academic publications, market research, news, not to mention social media feeds, all have text as a primary component and the volume of text is constantly growing. Apply the technology to voice and the pool gets even larger. Here are three examples of how organizations are putting the technology to work:

Edmunds drives traffic with GPT: The online resource for automotive inventory and information has created a ChatGPT plugin that exposes its unstructured data — vehicle reviews, ratings, editorials — to the generative AI. The plugin enables ChatGPT to answer user questions about vehicles with its specialized content, driving traffic to its website.

Eli Lilly overcomes translation bottleneck: With global teams working in a variety of languages, the pharmaceutical firm developed Lilly Translate, a home-grown NLP solution, to help translate everything from internal training materials and formal, technical communications to regulatory agencies. Lilly Translate uses NLP and deep learning language models trained with life sciences and Lilly content to provide real-time translation of Word, Excel, PowerPoint, and text for users and systems.

Accenture uses NLP to analyze contracts: The company's Accenture Legal Intelligent Contract Exploration (ALICE) tool helps the global services firm's legal organization of 2,800 professionals perform text searches across its million-plus contracts, including searches for contract clauses. ALICE uses "word embedding" to go through contract documents paragraph by paragraph, looking for keywords to determine whether the paragraph relates to a particular contract clause type.

Natural language processing software

Whether you're building a chatbot, voice assistant, predictive text application, or other application with NLP at its core, you'll need tools to help you do it. According to Technology Evaluation Centers, the most popular software includes:

Natural Language Toolkit (NLTK), an open-source framework for building Python programs to work with human language data. It was developed in the Department of Computer and Information Science at the University of Pennsylvania and provides interfaces to more than 50 corpora and lexical resources, a suite of text processing libraries, wrappers for natural language processing libraries, and a discussion forum. NLTK is offered under the Apache 2.0 license.

Mallet, an open-source, Java-based package for statistical NLP, document classification, clustering, topic modeling, information extraction, and other ML applications to text. It was primarily developed at the University of Massachusetts Amherst.

SpaCy, an open-source library for advanced natural language processing explicitly designed for production use rather than research. Licensed by MIT, SpaCy was made with high-level data science in mind and allows deep data mining.

Amazon Comprehend. This Amazon service doesn't require ML experience. It's intended to help organizations find insights from email, customer reviews, social media, support tickets, and other text. It uses sentiment analysis, part-of-speech extraction, and tokenization to parse the intention behind the words.

Google Cloud Translation. This API uses NLP to examine a source text to determine language and then use neural machine translation to dynamically translate the text into another language. The API allows users to integrate the functionality into their own programs.

Natural language processing courses

There's a wide variety of resources available for learning to create and maintain NLP applications, many of which are free. They include:

NLP – Natural Language Processing with Python from Udemy. This course provides an introduction to natural language processing in Python, building to advanced topics such as sentiment analysis and the creation of chatbots. It consists of 11.5 hours of on-demand video, two articles, and three downloadable resources. The course costs $94.99, which includes a certificate of completion.

Data Science: Natural Language Processing in Python from Udemy. Aimed at NLP beginners who are conversant with Python, this course involves building a number of NLP applications and models, including a cipher decryption algorithm, spam detector, sentiment analysis model, and article spinner. The course consists of 12 hours of on-demand video and costs $99.99, which includes a certificate of completion.

Natural Language Processing Specialization from Coursera. This intermediate-level set of four courses is intended to prepare students to design NLP applications such as sentiment analysis, translation, text summarization, and chatbots. It includes a career certificate.

Hands On Natural Language Processing (NLP) using Python from Udemy. This course is for individuals with basic programming experience in any language, an understanding of object-oriented programming concepts, knowledge of basic to intermediate mathematics, and knowledge of matrix operations. It's completely project-based and involves building a text classifier for predicting sentiment of tweets in real-time, and an article summarizer that can fetch articles and find the summary. The course consists of 10.5 hours of on-demand video and eight articles, and costs $19.99, which includes a certificate of completion.

Natural Language Processing in TensorFlow by Coursera. This course is part of Coursera's TensorFlow in Practice Specialization, and covers using TensorFlow to build natural language processing systems that can process text and input sentences into a neural network. Coursera says it's an intermediate-level course and estimates it will take four weeks of study at four to five hours per week to complete.

NLP salaries

Here are some of the most popular job titles related to NLP and the average salary (in US$) for each position, according to data from PayScale.

Computational linguist: $60,000 to $126,000

Data scientist: $79,000 to $137,000

Data science director: $107,000 to $215,000

Lead data scientist: $115,000 to $164,000

Machine learning engineer: $83,000 to $154,000

Senior data scientist: $113,000 to $177,000

Software engineer: $80,000 to $166,000

What Is Natural Language Processing?

Natural language refers to the regular speech and text that we use to communicate with each other. Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that enables computers to understand, interpret, and generate human language.

NLP bridges the gap between human communication and computer understanding by combining computational linguistics with machine learning, explains Arturo Buzzalino, Chief Innovation Officer, Epicor.

"AI includes other domains besides NLP, such as computer vision which deals with analysis and generation of images, but advances in NLP in the last few years have been at the heart of the current AI revolution," says Stefan Leichenauer, VP of Engineering, SandboxAQ.

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Describing NLP as the analysis and generation of natural language with computers, he says, it is the use of Large Language Models (LLMs) and chatbots that are driving a lot of the excitement around the subject.

NLP and LLMs

Drilling down further, Volodymyr Kubytskyi, Head of AI in MacPaw, says popular LLMs like OpenAI's ChatGPT or Google's BERT, are trained on massive amounts of text data, allowing them to grasp not just individual words but context, nuance, and even creativity in language.

He argues that it is these LLMs that have pushed NLP to new heights, enabling machines to generate coherent, human-like text, summarise long documents, translate between languages, and even engage in meaningful dialogue. By leveraging these models, NLP can now do things that seemed impossible a few years ago, like writing essays or answering complex customer inquiries in a natural, flowing manner.

"LLMs are the engine that's driving much of today's progress in making machines capable of human-like conversations," says Kubytskyi. "This is AI meeting language at an incredibly sophisticated level."

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Why should businesses care about NLP?

Leichenauer says because natural language is the way we communicate with each other, a lot of our business operations are encoded in natural language.

"Our reports and presentations, our internal memos and emails, and all of our customer communications are written in natural language," says Leichenauer. "NLP techniques can accelerate and automate workflows involving all of these things."

Building on this Buzzalino explains businesses should care about NLP because it allows them to extract meaningful insights from unstructured text data like customer reviews, emails, and social media posts.

NLP, he says, can help automate tasks such as customer support through chatbots, sentiment analysis for market research, and efficient document processing, thereby improving efficiency and enhancing customer engagement.

Sukh Sohal, Senior Consultant at Affinity Reply agrees. He says NLP brings real impact to businesses by transforming how they engage with customers, handle data, and even communicate internally.

"Imagine an AI that can analyze thousands of customer messages in minutes, picking up on common issues, emotions, or trends," says Sohal. "For companies, NLP can be the difference between overwhelming customer service demands and an efficient, responsive operation."

He says NLP lets businesses automate repetitive tasks, improve customer experience, and respond dynamically to feedback while freeing up human teams for tasks that require real insight.

Kubytskyi is excited about the use of LLMs and how it's elevating these NLP capabilities. For instance, he says, customer service bots

"This level of understanding allows businesses to offer personalized, responsive services without sacrificing efficiency," says Kubytskyi.

NLP applications

NLP has become so integrated into our lives that we often overlook it.

Buzzalino points to virtual assistants like Siri and Alexa that understand voice commands, customer service chatbots that handle inquiries, machine translation services like Google Translate, sentiment analysis tools that gauge public opinion on social media, and text analytics systems that extract key information from large volumes of documents, as some real-world applications of NLP.

One real-world application of NLP that strikes Leichenauer is as a smart assistant for writing code. This enables developers to operate more efficiently and also allows for low-code and no-code solutions that are more powerful than before.

How does NLP work?

Unlike traditional computing, which relies on straightforward commands, NLP involves teaching machines to grasp the subtleties and quirks of human language, including context, tone, and meaning, says Sohal. It's how AI moves from rigid rule-following to more intuitive understanding, opening up new ways for tech to interact with us in a more "human" way.

NLP is built on two key components. There's Natural Language Understanding (NLU), which analyses input to extract meaning and intent, and Natural Language Generation (NLG), which produces responses based on context and system logic, says Dan Balaceanu, Co-Founder & Chief Product Officer at DRUID AI.

For example, when a user requests to "book a flight to London," NLU identifies "book" as the action and "London" as the destination, while NLG generates a follow-up response, like "I found a flight to London for £220. Would you like to book it?"

Technically speaking, Sohal says, NLP works by breaking language down into patterns computers can recognize. It starts with tokenization, where sentences are split into words or smaller chunks. Then, grammar and structure are analyzed to understand the relationships between words.

Semantics come next, where computers use massive data to grasp meanings, even for slang or idioms. Finally, context and intent are added through machine learning, especially deep learning. "Here, NLP models learn from large datasets to identify emotions, requests, or subtleties in language, making responses more human-like," says Sohal.

Balaceanu adds this process standardizes vocabulary by reducing words to their root forms and filtering out common words that add little meaning, which helps to identify the real intent of the prompt that it should respond to, and how it should answer.

He adds that to improve the accuracy of the responses, NLP leans on machine learning techniques, such as deep neural networks, and models like transformers such as BERT.

"For NLP systems to respond accurately, they are trained on vast datasets that include diverse language patterns, grammar rules, and sentence structures, covering a range of possible queries and responses," adds Arunkumar Thirunagalingam, Manager, of Enterprise Data Management at Santander Consumer USA.

He says this training involves machine learning models and deep learning techniques that expose the AI to various linguistic scenarios, enabling it to recognize intent, context, and nuances. Over time, and with continuous learning from large, representative datasets, AI systems become more adept at handling complex language tasks and providing relevant, human-like responses.

What Is ChatGPT? The AI Natural Language Processing Tool Explained

This chatbot, which has both a free and paid version, launched by the research company OpenAI is one of the most prominent recent examples of artificial intelligence (AI).

In only a few months of existence, ChatGPT has quickly dominated conversations about AI and risen to international prominence. Users have adapted ChatGPT for a wide variety of uses, including to create workout plans, craft bedtime stories for children, and even to write scripts for television shows. Most importantly, ChatGPT has brought AI technology into mainstream awareness, providing a vehicle for people new to AI to imagine the possible ways that humans can interact with machines. In this article we explore what ChatGPT is and how it became popular so quickly.

Congress Restricts Staff Access to ChatGPT to Protect Privacy

ChatGPT is an example of an AI system that understands and produces conversational dialogue. It is a type of AI tool known as a chatbot, which many consumers will likely have encountered on the customer service websites for a variety of companies. OpenAI, the developer of ChatGPT, is a tech research company that aims to ensure artificial intelligence benefits all of humanity.

The name ChatGPT contains a reference to "Generative Pre-training Transformer," which indicates the way that Chat GPT processes language. A key element of the way that ChatGPT was trained—and something that sets it apart from chatbots over many years—is its use of reinforcement learning from human feedback (RLHF). RLHF is a process in which human AI trainers work with AI systems, providing rewards to help ChatGPT learn and correct its mistakes, to answer follow-up questions, and to automatically fix incorrect assumptions.

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In preparing this article, Decrypt asked ChatGPT to "write a paragraph explaining what ChatGPT is and how it works." In response, ChatGPT said:

ChatGPT is an advanced language model developed by OpenAI, built upon the GPT-3.5 architecture. It functions as a conversational AI, designed to engage in human-like text-based interactions. By leveraging deep learning techniques, ChatGPT has been trained on an extensive corpus of diverse internet text, encompassing a wide range of topics and styles. This training enables it to generate coherent and contextually appropriate responses based on the input it receives. ChatGPT operates by employing a transformer neural network, which allows it to process and understand the relationships between words, sentences, and concepts in the given text. It can infer meaning, recognize patterns, and generate relevant and coherent responses based on the context provided in the conversation. Additionally, OpenAI has implemented strategies to encourage safe and responsible usage of ChatGPT, including mitigating biases and providing user control over the system's behavior.

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To further explain some of ChatGPT's response, it is a system making use of deep learning, a process by which an AI tool examines a vast quantity of data to learn about common rules, style, and context. This, as well as the fact that ChatGPT utilizes a transformer neural network, allows it to not only understand the literal and contextual meanings of a wide variety of prompts, but also to create responses based on those prompts in a way that closely resembles human dialogue.

As mentioned above, users have already uncovered a tremendous variety of use cases for ChatGPT in its first months of existence. These uses run the gamut from minimal, chatbot-like functions to creative partners in a wide range of areas. ChatGPT can create content ranging from essays, think pieces, and articles to computer code, emails, and much more. The fact that ChatGPT remains available in a research-and-feedback-collection stage, meaning that it is free to the public to use, means that people everywhere can take advantage of its myriad uses. This is one reason why the Swiss bank UBS has called ChatGPT the fastest-growing app of all time, with 100 million active users just two months after its launch.

The easiest way to begin to access ChatGPT is to visit OpenAI's website and follow instructions to create an account. After you sign in, you'll immediately have access to ChatGPT and can begin a conversation by asking a question. While the system is still in a research phase, it is likely to remain free and available for all users.

There is also a subscription option for users wishing to guarantee access even while the platform is at capacity, or to connect ChatGPT to other tools through plugins. And OpenAI is launching ChatGPT apps, web browsing features, and more.

A major concern surrounding ChatGPT is safety and security. The tool is undeniably powerful, and it has led to rampant speculation about what might happen when bad actors make use of ChatGPT. Could terrorists use ChatGPT to falsify documents, or to hack into otherwise-secure networks? Could ChatGPT be put to work creating code that could be used for nefarious purposes? The system is built with many security protocols in place, but the huge range of potential uses may leave vulnerabilities.

Another concern regarding ChatGPT is inherent bias. Because ChatGPT has trained on human-generated documents and data, and because humans have implicit and explicit biases, OpenAI has said that its systems are prone to bias on at least racial and gender lines. The more popular ChatGPT becomes as a tool, the more these biases may feed back into the AI-generated content that humans read and use. Biases can also contribute to damaging and inaccurate responses.

Unsurprisingly, one of the early controversies regarding ChatGPT has been its potential use by students looking to cheat at school. ChatGPT recently proved that it can pass the bar exam, and universities have raced to put in place tools designed to determine whether student work is created with the use of ChatGPT or a similar generative AI tool. But a bigger concern may be that students can interact with ChatGPT to cheat in ways that are more subtle than having the chatbot take an exam or write a paper. For instance, ChatGPT can easily produce a thorough outline for a paper without writing the essay itself. An enterprising student can use ChatGPT's outline as the basis for a student-written paper that will easily pass through any detection system.

Although it's not a controversy exactly, one limitation to ChatGPT is that it has been trained on data up to a certain time only. This means that ChatGPT may not be aware of the latest news and developments, potentially leading to inaccurate or incomplete responses to certain prompts.

ChatGPT does not typically ask for clarification when a prompt or question is ambiguous, and instead makes a guess about what the question means. This can lead to unintentionally incorrect responses. Stack Overflow moderators have said that a problem with the system is that it produces answers that "have a high rate of being incorrect," while typically looking like they "might be good…and the answers are very easy to produce." ChatGPT is, after all, putting words together in order that matches up with the data it has been trained on. This does not, however, necessarily mean that it understands a question or that it is answering correctly.

AI experts have speculated that ChatGPT could fundamentally transform the way that people interact with computers. ChatGPT may replace traditional search engines by providing users a more interactive, more responsive, and generally more accurate and functional search experience. This is perhaps one reason why Microsoft moved quickly after the release of the chatbot to launch a version of its Bing search engine that is

Though ChatGPT is not without its controversies, as illustrated above, many feel that its possible uses are seemingly endless. What's more, OpenAI is continuing to develop the technology behind ChatGPT—and a host of other generative AI tools are also in development, pushing the AI space even farther forward.

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