NLP vs LLM: What Are They and How Do They Work?

In the world of technology and artificial intelligence (AI), two terms that frequently emerge are NLP (Natural Language Processing) and LLM (Large Language Models). This article will explore what they are and how they function, highlighting their synergies.

What is NLP?

Natural Language Processing, or NLP, is a field of artificial intelligence focused on developing algorithms and models to teach machines to understand, interpret, and generate text. In other words, NLP enables interaction between machines and human language.

What is NLP Used For?

The applications of NLP are incredibly diverse and have a significant impact on our daily lives. Virtual assistants like Siri, Google Assistant, and Alexa use NLP to understand and respond to our questions, making our interactions with technology more natural and intuitive. Automatic translation services, such as Google Translate, leverage NLP to translate text from one language to another, breaking down language barriers. Another application area is sentiment analysis, which uses NLP to determine the tone and emotions in product reviews, social media comments, and customer feedback. NLP is also applied in technical text analysis, particularly in extracting strategically relevant data. This is especially useful when processing documents such as patents and scientific publications, enabling the identification of growing technologies or topics of interest and investment, thus providing a reliable and comprehensive overview of innovation investment trends.

How Does NLP Work?

As mentioned, NLP involves developing algorithms and models to understand and generate text. Specific algorithms and models are used depending on the linguistic task: from rule-based algorithms (e.g., lexical matching) to AI-based algorithms (linear SVMs, Neural Networks, LSTM, Transformers), which allow performing linguistic tasks on texts not previously analyzed by machines. In the case of AI algorithms, specific data preparation, known as “training corpora,” is necessary to provide the machine with knowledge and enable it to learn and replicate a particular action. For example, if the goal is to teach a machine to recognize a positive tone from a negative one in an online article, it will be necessary to provide the machine with precise examples of what is negative and what is positive, with a series of manually labeled texts in the two categories POS and NEG. Once the corpus is processed, the machine will be able to “discern,” for instance, a compliment from a critique, thus generalizing this behavior on new texts. With the same techniques, it is possible to extract relevant information (Information Extraction), such as in technical document analysis, entities like product features, technology functions, advantages, and disadvantages of an invention.

What is an LLM?

Another significant technology is Large Language Models (LLMs), a specific category of deep learning models, such as OpenAI’s GPT-3, designed to understand and generate text with fluidity and coherence that often approaches human levels.

How Does an LLM Work?

At the heart of an LLM are deep neural networks based on transformer architecture. Introduced in 2017 with the model “Attention is All You Need,” this architecture revolutionized natural language processing. Transformers use attention mechanisms that allow the model to weigh the importance of different words in a text, improving the understanding of context and semantic relationships. This is particularly useful for long and complex texts, where word relationships are not always immediately apparent. The training process of an LLM begins with a pre-training phase on massive amounts of textual data from various sources such as books, articles, and websites. During this phase, the model learns the rules of language, word associations, and the context in which they are used. Once pre-trained, the model can be further refined for specific tasks through a fine-tuning phase, using smaller and specialized datasets. This process enables the model to better adapt to specific tasks like translation, question answering, or generating coherent text.

What is an LLM Used For?

The applications of LLMs are numerous and often surprising. They can be used to create virtual assistants that understand and respond to complex questions, improve automatic translation services, making them more accurate and natural, and generate textual content for articles, blog posts, emails, and more. Additionally, LLMs can be employed in data analysis, helping to extract useful information from large amounts of unstructured text, such as customer reviews or social media posts. In summary, Large Language Models represent a powerful resource in artificial intelligence, offering advanced capabilities in language understanding and generation. Their ability to adapt to a wide range of applications makes them indispensable tools for improving human-machine interaction and automating complex language-related tasks.

NLP and LLM Relationship

There is a strong synergy between NLP and LLM. NLP (Natural Language Processing) is a broader field that encompasses various techniques and approaches for processing natural language, while LLM (Large Language Model) is a specific type of machine learning model used within NLP. In other words, LLM is a tool used in the field of NLP to perform natural language processing tasks. In summary, NLP deals with the interaction between computers and human language, while LLM is a type of machine learning model used within NLP to learn and understand human language. The application of these techniques allows companies to gain a significant competitive advantage, enabling them to make more informed decisions, optimize processes, and innovate more effectively.