Natural language processing. Large language models. Two terms you’ve probably seen tossed around in AI conversations. Sometimes they’re even used like they mean the same thing. But they don’t.
Long story short, NLP is the broader field of teaching machines to understand human language. LLMs are one (very powerful) outcome of that field. You can think of NLP as science, and LLMs as one of its most advanced tools.
Curious to learn more? Keep reading to discover where the confusion comes from, how the two are connected, and what this means for anyone working with AI, content, or localization.
🧠 Learn more about NLP, LLMs, and the future of language AI
At Lokalise, we’re closely following how AI is reshaping the way teams handle language. From smarter translations to more adaptive content workflows, there’s a lot going on. LLMs may be getting all the buzz, but understanding the roots in NLP gives you a clearer view of what’s possible (and what’s not).
Why people confuse NLP and LLMs
LLMs are built using NLP techniques, so people sometimes think they are synonyms. However, the key difference is that NLP is the broader field, and LLMs are just one type of tool within it.
It’s easy to see why these two terms get tangled. LLMs like ChatGPT or Gemini are everywhere, and they often get mentioned in the same breath as NLP (even though they’re not the same thing).
❗ Important note
In the context of NLP vs. LLMs debate, it’s important to realize the two terms are connected, but not interchangeable.
When people refer to an LLM and lump it under “NLP”, they’re not wrong. It’s just that they’re not being very specific. It’s like calling a smartphone a “computer”. This is technically true, but not quite precise.
Additionally, not all NLP involves large models. In fact, plenty of NLP tasks rely on simpler methods that don’t require deep learning at all. Think spell check or keyword extraction. The rules they rely on are straightforward (e.g., basic statistical techniques).
What is natural language processing (NLP)?
NLP, or natural language processing, is the science behind how computers understand and work with human language. It’s not new. As a matter of fact, researchers have been working on it since the 1950s.
Back then, they tried building systems to translate Russian into English using hand-written rules. The results were limited, but it kicked off decades of progress.
Today, NLP is everywhere.
It helps your email sort out spam, powers predictive text when you’re typing, and lets voice assistants like Siri or Alexa understand what you’re saying. For example, when you ask Alexa to play your favorite playlist, NLP is what helps it figure out what you mean and respond the right way.
What are large language models (LLMs)?
LLMs, or large language models, are a newer (and flashier) development. They’re built using NLP techniques.
LLMs are trained on huge amounts of text. Think books, websites, forums, and all kinds of online content. As they read through all this data, they learn patterns in how people write and speak. That’s how they’re able to respond in a way that sounds natural and makes sense in context.
Sometimes, their replies can feel surprisingly human, and this is especially evident in the translation industry. AI translation tools built on top LLMs often outperform traditional machine translation tools.
How LLMs fit into the NLP landscape
LLMs are part of NLP. But they’re not the whole story.
Natural language processing, or NLP, is a broad field that covers all the ways computers learn to understand and work with human language. It includes everything from simple systems that recognize certain phrases, to lightweight tools that detect spam, pull out keywords, or figure out whether a word is a noun or a verb.
Large language models came later, building on top of this foundation. Thanks to the transformer architecture introduced by Google in 2017, it became possible to train much larger models that could understand context on a much deeper level.
So, while LLMs rely on NLP techniques, they represent a specific and powerful approach within the field. You can think of NLP as the umbrella and LLMs as one of the most advanced tools developed under it.
💡 Good to know
A big milestone for modern LLMs came in 2018 when Google introduced a model called BERT. What made BERT special was that it could understand the words around a sentence (so, not just what came before, but also what came after). That gave it a much better sense of context compared to older models.
Key differences between NLP and LLMs
Now that we’ve looked at how LLMs fit into the NLP landscape, let’s break down what actually sets them apart. While LLMs are a type of NLP system, they operate on a different scale, and therefore they have different strengths and limitations.
Task complexity and scope
| Natural language processing (NLP) | Large language models (LLMs) |
| Designed for specific, narrow tasks | General purpose, can handle a wide range of tasks |
Traditional NLP systems are usually designed for specific, narrow tasks. They might be tasked with identifying names in a sentence, checking grammar, or classifying emails as spam. As you realize, they’re 1) purpose-built and 2) often limited to one function at a time.LLMs, by contrast, are general-purpose.
Once they’re properly trained, they can handle a wide range of tasks. They can write emails, summarize reports, answer questions, or translate. There is no need to reprogram them for each individual task. The same model can shift from creative writing to writing executive summaries with just a different prompt.
🤖 A few words on AI translation
Ever wondered what happens when you use an AI translation tool and click “Translate”? Words in a new language appear magically on your screen, but there’s quite a lot happening in the background. Learn more about how AI translation works.
Data and training requirement
| Natural language processing (NLP) | Large language models (LLMs) |
| Trained on modest, task-specific datasets | Data-hungry, trained on enormous datasets |
Most classic NLP models can be trained on modest, task-specific datasets. They don’t require much computing power. This makes it easy to run them locally or embed them in lightweight applications.
LLMs are different. They are data-hungry and require more resources. They’re trained on enormous datasets (e.g., books, websites, forums), and require powerful hardware (like GPUs or TPUs) over days or weeks. It’s not so simple to train them. In fact, it’s quite a venture to do so.
Model architecture and capabilities
| Natural language processing (NLP) | Large language models (LLMs) |
| Simple architecture | Transformer architecture |
Traditional NLP models often rely on simpler architectures, like decision trees, logistic regression, or sequence models. These can struggle with ambiguity or long-range dependencies in text.
LLMs are built on transformer architectures, which can handle long context windows and capture complex relationships between words. This allows them to generate language that’s not just grammatically correct, but also contextually coherent (sometimes very close to how a human would write).
◀️ Recap on transformer architecture
Older models read text one word at a time, like reading with blinders on. Transformers can look at all the words at the same time, figure out how they relate to each other, and focus on what matters most. That’s what makes them so good at handling complex language tasks like writing, translating, or answering questions.
Context handling and output generation
| Natural language processing (NLP) | Large language models (LLMs) |
| Output is task-specific and structured | They generate language and can produce entire paragraphs |
With classic NLP, output is usually task-specific and structured. It’s about label content as “positive” or “negative” in sentiment analysis, generating a list of keywords from a product description, or identifying grammatical roles in a parsed sentence. These systems don’t generate language in the way you might expect from an AI assistant.
LLMs, in contrast, are designed for language generation. They can craft entire paragraphs, hold conversations, and adapt their tone and style based on the input. They’re producing new language based on everything they’ve seen during training.
NLP vs. LLMs in action: Use cases, strengths, and limitations
NLP and LLMs both help machines work with language, but they’re suited to different kinds of tasks:
- Traditional NLP is often faster, cheaper, and easier to deploy (especially for specific and well-defined problems)
- LLMs are powerful when context and human-like output matter
Let’s look at where each one fits best.
NLP is ideal for:
- Flagging spam emails based on keywords and patterns
- Categorizing customer support tickets by topic
- Extracting names, dates, or locations from documents
- Running basic sentiment analysis (for example, on customer reviews or surveys)
- Building rule-based chatbots for FAQs
LLMs are ideal for:
- Drafting emails, blog posts, and product descriptions
- Summarizing long reports or meeting transcripts (great for TL;DR summaries and overview of next steps)
- Translating content while preserving tone and nuance (especially if you share context with AI)
- Powering chatbots that can adapt to human-like dialogue
- Assisting with multilingual customer support at scale
🤖 LLMs in translation
LLMs are the backbone of AI translation tools. Thanks to them, these tools are capable of handling idioms, complex sentence structures, and even informal language with great fluency. That’s why they’re increasingly used in localization workflows.
With the development of translation technology, it’s becoming very challenging to decide on the best way to handle localization. Should you outsource or take everything in-house, should you stick with linguists or trust AI to do the heavy lifting?
Strengths and limitations
Want to see strengths of NLP vs. LLMs in a single glance? Check out the table below.
| NLP | LLMs | |
| Best for | Simple, structured tasks | Complex, flexible, context-rich tasks |
| Speed & cost | Fast, lightweight; low resource use | Slower; expensive to train and run |
| Data requirements | Works with smaller datasets | Requires massive datasets and computing power |
| Output | Labels, keywords, or structured results | Full sentences, paragraphs, and conversations |
| Customization options | Easy to tailor for specific tasks | Can adapt through fine-tuning |
Choosing the right tool: NLP, LLM, or both?
When we talk about “using NLP” or “using an LLM,” we’re really talking about choosing tools or platforms that are built on these technologies (don’t worry, you won’t need to code models from the ground up).
If your goal is to automate simple, rule-based tasks, like tagging content, routing support tickets, or detecting spam, then NLP-powered tools are usually the right fit. These are lightweight, efficient, and designed for specific tasks.
Think things like:
- Text classification tools built into your CRM
- Sentiment analysis features in social listening software
- Language detection APIs in translation management systems
But if you need to generate text and understand context-rich input, you’ll want a tool powered by LLMs. This could include:
- AI writing assistants like Jasper, Notion AI, or Copy.ai
- Generative chatbots like ChatGPT, or customer support tools powered by GPT
- Translation tools that use LLMs for more natural output, like Lokalise AI
LLM vs. NLP examples: AI-powered localization workflow
Let’s say you’re localizing your website or app into five languages. Here’s how NLP and LLMs can work together to make this AI localization process faster, smarter, and a lot less manual.
Pulling out the right content (NLP)
First, NLP helps detect what needs to be translated. It can scan your site or app, pull out translatable strings, and even tell the difference between things like button text, error messages, or product descriptions. It might also flag repeated phrases to avoid translating the same thing twice.
Translating with context and fluency (LLM)
Then, an LLM steps in to do the heavy lifting. Instead of translating word-for-word, it rewrites the content in a way that sounds natural in the target language. It can adapt the tone, style, and phrasing.
Checking for consistency and brand voice (NLP again)
After the LLM generates the translation, NLP tools can help check grammar, make sure brand terms are used correctly, and flag anything that seems off in tone or meaning. Think of it as a smart second pair of eyes.
Learning and improving over time (both)
If a human editor steps in and tweaks something, the system can learn from that. NLP helps track the changes, and the LLM can adjust future translations based on that feedback.
Future outlook: Are LLMs the evolution of NLP?
LLMs are definitely a big leap forward in the NLP world, but they didn’t replace it. They’re built on top of NLP techniques. So, they just scaled up with more data and smarter architecture.
LLMs have opened the door to things like natural-sounding translations, long-form content generation, and AI chatbots that feel human. But they’re not perfect. Just remember that they are resource-heavy, harder to control, and sometimes overkill for simple tasks.
However, new tools are emerging that give users more control over tone, accuracy, and output, and that blend the reliability of NLP with the creative, generative power of LLMs.
So, what’s ahead? Looks like a steady evolution. Language AI will become more personalized, more modular, and more tightly integrated into everyday tools and workflows.
What does this mean for your business
You no longer need a team of data scientists to benefit from NLP or LLMs. Modern tools package that power into intuitive platforms you can easily plug into your workflows.
Need faster customer support, smarter localization, or content that adapts to your audience? You’ve got options. And as tools continue to blend NLP’s precision with LLMs’ fluency, the gap between “automated” and “human-like” will keep getting smaller.
This means we’re moving towards interacting with AI that actually understands us. Not just what you type, but also how you say it, what you mean, and what you’re trying to do.
Eerie or exciting? Depends on your perspective.For more articles on LLMs, AI, and translation and localization, make sure to check the Lokalise blog.
FAQs about LLMs and NLP
No, LLMs are actually a subset of NLP. NLP is the broader field that teaches machines how to understand and work with human language. LLMs are just one type of NLP model, designed to handle more complex tasks like writing and conversation.
NLP covers all the ways machines work with language. LLMs are one powerful part of that. They use huge amounts of data to understand context and generate human-like responses.
LLMs are not replacing NLP. LLMs are great for creative, flexible tasks, but traditional NLP tools are still better for fast, specific jobs like tagging text or detecting sentiment. It’s often best to use both together.
NLP handles things like tagging keywords or filtering spam. Those are all very quick, rule-based tasks. For example, your email app might use NLP to spot junk mail. But LLMs go further. They can write a full email, translate text naturally, or chat with you like a real person.
