Data Science

What Are Large Language Models (LLM)?

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By Pere Munar, on 13 September 2023

When you hear the term large language models, you might be quite sure what this means but what if you ask you ChatGPT?

In this article, we'll take a closer look at these AI and data science tools to find out how they work and all the benefits they can bring to your company.

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What Are Large Language Models LLM


What Are Large Language Models (LLM)?

Large Language Models, or LLM, are neural networks capable of reading, translating, and summarizing texts, thus being able to create sentences and predict words as if they were written or spoken by a human.

This type of AI has been trained with a huge amount of data and millions of words, which has allowed it to recognize word patterns and learn about language and its natural and contextual use.


Examples of 3 Large Language Models

Large language models are becoming increasingly popular, mainly due to models such as ChatGPT from the OpenAI company. Below, we want to show you some of the most powerful ones.



ChatGPT3

This LLM is trained on approximately 570GB of text data from a public database known as CommonCrawl. ChatGPT3 currently has one of the largest neural networks on the market and can reproduce any type of text with a given structure.


Turing NLG

Turing NLG came out in 2020 and was for a long time the largest LLM of its kind, counting 17 billion parameters. Developed by Microsoft, it can produce words to finish an incomplete sentence, summarize texts and answer questions.


Gopher

The Gopher LLM excels in massively multitasking language understanding. It is a 280 billion parameter model developed by DeepMind.


How Can You Apply Large Language Models at Your Company?

There are many aspects in which great language models can help a company, here are some of the most relevant ones:

  • Support for copywriters and content creators: Large language models are able to create texts from scratch that are adapted to the needs of professionals, give creative ideas, and rewrite texts. Although they cannot replace the work of a copywriter yet, they can be a great support in their daily work. They can even be a great ally when proposing topics for a content marketing strategy.
  • Text translation: They can translate texts into any language.
  • Planning tool: In addition to creating texts, they have task organization capabilities.
  • Customer service chatbot: Many companies already use this type of large language model AI as the first step in customer service. Surely you have already seen on many websites or applications how the first response you get is from an AI, which is able to maintain a conversation and solve simple problems and then to refer you to a professional if you need it.
  • Ally for programmers: Engineers and computer scientists can also benefit from LLMs, since they can solve issues in codes and programming. This will make work more agile, as it is more efficient and faster to ask questions to big language models than it is to search in programmers' forums such as Stack Overflow.
  • Cybersecurity: AI can be a great ally in the fight against cyber attacks.


Benefits of Large Language Models

There are several advantages that LLMs can provide. Due to their unsupervised machine learning, they are able to learn from unlabeled data to perform tasks such as text creation or machine translation.

Also, because they handle large amounts of data, they learn language structure. And, last but not least, they are multipurpose, meaning that they can be used in different tasks, as we have seen above.


The Other Side of LLMs

Despite all the advantages we have covered so far and all the advances that large language models have brought to the world, all that glitters is not gold. LLMs are not cheap, as large amounts of data are needed to train them. In fact, this training can take a long time, since they are very complex models, so it isn't the most agile process. Even the implementation of LLMs is not easy as it requires specialized software.

However, these drawbacks are not only found in large language models, but are present in all machine learning models. The difference between LLMs and the rest is that they perform better in very diverse and day-to-day tasks.


How LLMs Learn

Virtually all major language models are trained with a large amount of text data. But within this training, we find two styles:

  • The BERT or masked style: Where from a text segment, such as "I am passionate about (...) (...) on the beach", the model predicts masked words, in this case "playing" and "sports".
  • The GPT or autoregressive style: In this case, you start from a text, such as "I don't like dancing", and the model predicts the following word "ballet".

We hope this has shed some light on large language models and that you have learned more in depth what is behind tools like ChatGTP in a more technical sense.

From Cyberclick, we encourage creating dynamics within your company where you rely on this type of Artificial Intelligence, not as a substitute for professionals, but as an ally to enhance the creative and technical processes, and the agility and efficiency of the company. In the future, they will become one more tool that your team uses to do their daily tasks.

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Pere Munar

Data Scientist en Cyberclick. PhD en Astrofísica por la Universitat de Barcelona con más de diez años de experiencia en investigación mediante el análisis e interpretación de datos. En 2019 redirige su carrera profesional hacia el mundo del Data Science cursando el Postgrado en Data Science y Big Data de la UB, así como participando en el programa Science To Data Science (S2DS) en Londres. Actualmente forma parte del equipo de Data Science y SEM de Cyberclick.

Data Scientist at Cyberclick. PhD in Astrophysics from the University of Barcelona with more than ten years of research experience through data analysis and interpretation. In 2019 he redirected his professional career to the world of Data Science by graduating in Data Science and Big Data from the UB, as well as participating in the Science To Data Science (S2DS) program in London. He is currently part of Cyberclick's Data Science and SEM team.