LLM vs NLP | Large Language Model vs Natural Language Processing

Introduction

In recent times due to OpenAI ChatGPT, the term LLMs (Large Language Models) has garnered mainstream recognition. Whereas NLP (Natural Language Processing) has been around as a discipline for many decades. Since LLMs can be used for almost anything related to text, at first glance it looks as if the term LLM can be used interchangeably with NLP. However, on closer comparison, the differences between the two come out. In this article, we will do a thorough comparison between LLM vs NLP for a better understanding for beginners.

LLM vs NLP

Definition

• LLMs are statistical models used for predicting the next word in a sequence, based on the previous words. In essence, they use probabilities for word sequences, to generate human-like text.
• NLP is a broader field within artificial intelligence and computational linguistics. It focuses on enabling machines to understand, interpret, and create human language. NLP encompasses various tasks such as sentiment analysis, machine translation, text mining, text summarization, named entity recognition, POS tagging, etc.

Background

• While the concept of predicting sequences isn’t new, the advancements in deep learning in the late 2010s gave rise to powerful LLMs, like GPT-3 and BERT. These models can generate remarkably human-like text, making them cornerstones in the AI industry.
• NLP’s roots can be traced back to the 1950s and 1960s. The famous Turing Test, proposed by Alan Turing, was one of the earliest challenges for NLP. It set the challenge for machines to interact with human-like language capabilities.

Key Difference

The most important difference between LLM vs NLP is their scope. LLM is a subset of NLP and deals primarily with text prediction & generation. Whereas NLP deals with a vast array of tasks beyond LLMs, like sentiment analysis, machine translation, text mining, text summarization, named entity recognition, etc.

Overlap

The difference between LLM and NLP is becoming blurred nowadays. This is because most of the NLP tasks can be accomplished with LLMs instead of going with traditional methods. For instance, tasks like question answering, named entity recognition, and question answering can now easily be done with LLMs like GPT, BERT, etc.

Computational Cost

• LLMs, especially those based on deep learning architectures, require significant computational resources. State-of-the-art models like GPT-3 consist of billions of parameters and demand powerful GPUs. Needless to say this means it can be very expensive to train a LLM model.
• On the contrary, most of the traditional NLP tasks can be executed efficiently without any need for GPU horsepower, and hence are cost-effective options.

Practical Applications

Although LLMs can do basic NLP tasks, it is more known for advanced tasks like content generation, coding assistance, advanced conversational chatbots, analytical & logical reasoning, etc.

On the other hand, NLP is more known for tasks like sentiment analysis, machine translation, text mining, text summarization, named entity recognition, POS tagging, etc.

Summary of LLM vs NLP

The below table presents the summarized view of LLM vs NLP based on the points we discussed above.