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.
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.
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.
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 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.
The Gopher LLM excels in massively multitasking language understanding. It is a 280 billion parameter model developed by DeepMind.
There are many aspects in which great language models can help a company, here are some of the most relevant ones:
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.
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.
Virtually all major language models are trained with a large amount of text data. But within this training, we find two styles:
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.