NLP vs LLM: differences between two related concepts

Natural language processing has recently become inextricably linked with large language models. These two things have a lot in common, but they represent different concepts with many distinct features. Let's figure out how they are different and what binds them together and allows them to work seamlessly alongside each other.

What is natural language processing? 

A branch of artificial intelligence and computer science called natural language processing (NLP) exists to teach systems to interpret human language. It involves techniques and algorithms to process, understand, and generate it. NLP is a group of methods for automatically processing natural human language in spoken or text format. By focusing on the interaction between computers and human natural languages, NLP incorporates a wide range of techniques to make computers produce human language, including:

• Text classification ;

• Sentiment analysis;

• Named Entity Recognition (NER);

• Language translation or machine translation;

• Speech recognition;

• Parsing;

• Semantic analysis;

• Content creation;

• Stemming.

NLP stands at the crossroads of computer science and linguistics, pulling from both fields to tackle the everyday nuances of language, which can often be ambiguous and context-dependent.

What are large language models?

Conversely, Large Language Models (LLMs) are a specific type of AI model that has transformed NLP. These models, like OpenAI's GPT-4, Google's BERT, or Meta's LLaMA, use deep learning techniques to analyze vast amounts of data. They’re built on neural network architectures, particularly transformers, enabling them to understand text at a scale that traditional NLP methods can’t match.

LLMs are trained on diverse textual datasets, ranging from books and websites to social media posts. They learn patterns, structures, and even nuanced meanings in language. This allows them to perform tasks like language generation, translation, and summarization with minimal human intervention.

LLMs nowadays can generate human-like text and perform complex language tasks, revolutionizing how we interact with machines. One of the most visible applications of NLP and LLMs is in virtual assistants like Siri, Alexa, and Google Assistant. These virtual assistants rely on a combination of NLP techniques and increasingly sophisticated LLMs to recognize and respond to user commands.

Key differences between NLP and LLM

Techniques Used

NLP employs a variety of approaches:

• Rule-based systems that follow specific grammar rules;

• Statistical methods that predict language patterns;

• Classical machine learning models, like decision trees or support vector machines (SVMs);

LLMs, however, rely on deep learning. They use transformer architectures with self-attention mechanisms to process vast amounts of text, learning complex patterns and contextual relationships without predefined rules.

Scope and Purpose

NLP is a broad field focused on how machines interact with human language. It includes various methods to process, understand, and generate language. LLMs, on the other hand, are just one very advanced tool within that field, designed to predict and generate human-like text based on the vast amounts of data they’ve been trained on. NLP encompasses all the methods used to handle language, and LLMs are large-scale models that can execute many of these tasks in a more advanced way.

Understanding vs. Prediction

NLP techniques traditionally focus on processing language by breaking it into relevant components like syntax, grammar, and semantics. These methods aim to analyze the structure and extract patterns from the text. For instance, NLP models might use linguistic rules or statistical techniques to identify sentence structure, recognize named entities, or figure out relationships between words in a sentence.

However, it's important to note that NLP models don't truly "understand" language like humans do. They analyze text based on patterns without a deeper understanding of the meaning.

LLMs, on the other hand, are often described as sophisticated prediction machines. They produce text based on probabilities learned from vast amounts of data. While LLMs do not explicitly extract patterns as traditional NLP models do, they implicitly learn complex patterns during training, which inform their predictions.

LLMs predict what word or phrase is most likely to come next in a sequence based on the patterns they've encountered in the data and the context surrounding the text. In that way, they generate new data that resembles their training data. These newly predicted responses are usually remarkably coherent and contextually appropriate, so LLM's primary function remains to predict new information based on the training data, not explicit pattern extraction.

To summarize, NLP techniques are about pattern extraction and analysis, focusing on structure rather than meaning. LLMs are focused on predicting new data, but those predictions rely on patterns learned implicitly during training, though they do not perform the explicit pattern extraction that traditional NLP does.

How LLM and NLP Work Together

NLP can be seen as the framework or mechanics that help machines process and analyze language. It encompasses the fundamental techniques and methods needed to break down human language into a form that computers can interpret. These foundational processes allow machines to analyze basic linguistic structures, helping them to classify text, recognize entities, or convert speech to text.

LLMs build on this foundation and enhance their ability to generate coherent and contextually relevant text based on their learned patterns. They use advanced transformer architectures and are trained on massive datasets, allowing them to model language in a broader context and resulting in more sophisticated text generation.

This ability to model context effectively allows LLMs to generate coherent, human-like text, predict what comes next in a sentence, and mimic subtleties such as tone, sentiment, and intent based on the patterns they have learned from data.

While NLP provides the basic techniques for processing and analyzing language without true comprehension, LLMs significantly enhance the ability to generate fluent, contextually relevant text by leveraging patterns learned from large datasets. However, it’s important to note that LLMs also do not genuinely understand language or context. They model these aspects based on statistical likelihood.

Training phase

LLMs leverage NLP techniques during training, where they are fed massive text datasets. They learn patterns in a language, such as syntax, semantics, and grammar, by adjusting internal parameters to predict the next word in a sentence, fill in blanks, or classify text. While LLMs can recognize patterns that align with factual information from the real world, they don't learn facts like humans do.

Understanding language

NLP techniques like tokenization and parsing are critical steps in how LLMs process language. LLMs use these tokenized inputs and model the relationships between them, understanding context through attention mechanisms. Doing so allows them to recognize meaning, disambiguate words, and handle complex language structures.

Language generation

NLP tasks like text generation, summarization, and machine translation rely on LLMs to generate coherent, contextually appropriate text. LLMs predict and generate text word by word or token by token based on the input context, effectively utilizing their training to generate linguistically accurate responses.

Transfer learning

Modern LLMs are often pre-trained on massive datasets in an unsupervised way, which entails learning from raw text without labels and then fine-tuning for specific NLP tasks like sentiment analysis, question-answering, or translation. During pre-training, the LLM captures general linguistic knowledge or rather general patterns in language structure, which is then adapted to specific NLP applications during fine-tuning.

Handling complex Tasks

NLP tasks require processing not only words but also their nuanced meanings in context, handling idiomatic expressions, or even multi-step reasoning. Thanks to their deep layers and attention mechanisms, LLMs are capable of capturing long-range dependencies in text, making them well-suited for these tasks.

LLMs as a core technology for NLP

LLMs and NLP are deeply interconnected, so one cannot exist without the other in practical AI applications. Large Language Models are now central to most advanced NLP applications. Without LLMs, achieving the accuracy, fluency, and versatility we see in language generation, translation, and question-answering would be difficult. The traditional rule-based systems or statistical NLP models couldn't handle the complexity of human language at the scale that LLMs can.

LLMs, especially those based on transformer architectures, are designed to grasp context and relationships across texts. This understanding is a core part of NLP, which would be far more limited without LLMs.

LLMs are trained on diverse datasets to learn the intricacies of language on a large scale. Processing human language relies heavily on this pre-learned knowledge to understand different languages, idiomatic expressions, or the ambiguity of natural language.

NLP provides structure for LLMs

Conversely, LLMs would not be as useful without NLP. NLP provides the structure, techniques, and objectives that shape how LLMs are applied. While LLMs are pre-trained on broad data, NLP techniques and task-specific datasets help fine-tune them for specialized tasks. LLMs would remain general-purpose but less effective in domains requiring specialized knowledge without fine-tuning on specific tasks.

Also, NLP methods like tokenization, stemming, and parsing are useful for preparing text data in a form that LLMs can process. These models require tokenized and structured inputs to function effectively, and NLP preprocessing steps play a vital role in transforming raw text into a usable format.

NLP provides ways to evaluate and validate the performance of LLMs on specific language tasks. Metrics like BLEU score for machine translation, F1 score for classification tasks, or perplexity for language generation are rooted in NLP methodologies.

Synergy in application

In real-world applications, NLP is the field that applies the advances in LLMs. This symbiotic relationship is crucial because, without LLMs, modern NLP would revert to more traditional, rule-based, and statistical systems that struggle with complexity and large-scale language tasks.

Without NLP, LLMs would lack the practical applications and structured guidance that make them useful in solving specific language problems, such as summarizing documents or translating text.

Benefits of LLMs in NLP

Large Language Models have made a huge difference in the field of Natural Language Processing, drastically changing how we work with human language.

Improving NLP with natural text generation

Thanks to LLMs, one of the biggest wins for NLP is their ability to generate text that sounds human-like and contextually accurate. Whether you're working on chatbots, email automation, or even creative writing, LLMs can predict what comes next in a sentence and produce text that flows naturally. This has made many NLP tasks, like content generation or customer support systems, far more effective and user-friendly.

Better language understanding in NLP

NLP is about teaching machines to understand human language, and LLMs have taken this to a new level. LLMs can pick up on the subtle relationships between words, phrases, and sentences, making them much better at interpreting complex language.

This is a giant leap forward from older NLP methods, which often struggled with longer sentences or missed context. With LLMs, machines can process not just individual words but entire paragraphs, getting a much deeper understanding of the text.

Handling ambiguity and polysemy

Languages are ambiguous by nature, and many words have multiple meanings depending on their context, which is known as polysemy. Traditional NLP systems often struggled to disambiguate words like "bank," which could refer to a financial institution or the side of a river, or "run," which can be used in various ways depending on context.

LLMs excel at resolving these ambiguities by leveraging the surrounding text. Thanks to the vast amounts of data they are trained on, they can accurately disambiguate polysemous words and make better sense of nuanced, context-dependent meanings.

This improvement is essential for NLP tasks like Named entity recognition (NER), where distinguishing between different meanings of names, locations, or organizations is important, or question answering where the correct interpretation of a word or phrase directly impacts the answer.

Multimodal capabilities in NLP

Some LLMs are now being extended to handle multimodal data, such as text and images combined. This development is a major boost for NLP because it allows language models to work in applications that mix text with other forms of media.

For instance, an LLM can describe an image's contents, answer questions about images based on textual context, generate descriptive text for videos, or produce visual content from text prompts.

These multimodal capabilities are pushing the boundaries of what NLP can do. They can enable more advanced human-computer interaction and improve areas like accessibility and tasks like automatic image descriptions for visually impaired users.

Scalability for massive data processing in NLP

LLMs have proven highly scalable, making them perfect for large-scale NLP applications. In sectors like healthcare, legal, or finance, organizations often need to process huge volumes of text data, including research papers, legal contracts, and customer feedback.

LLMs can efficiently analyze, extract information, and generate insights from this vast data. Their ability to scale across massive datasets has made it possible to automate what was once highly manual, labor-intensive processes, such as:

• Legal document review. Identifying key clauses, summarizing contracts, or spotting discrepancies;

• Medical research. Summarizing research papers or extracting relevant medical information;

• Customer service. Automatically processing and responding to customer inquiries at scale.

Continuous evolution together

The success of LLMs is driving further advancements in NLP and vice versa. As LLMs become more powerful, NLP tasks can handle more challenging problems. At the same time, new NLP tasks and goals like better reasoning, understanding, or even creativity in language push the boundaries of LLM architectures.

In essence, LLMs are the backbone of modern NLP, and NLP gives purpose and structure to LLMs' capabilities. Neither would be as powerful without the other, making their relationship essential for modern AI systems in language understanding and generation.