What Is Large Language Models

Written by humans or by artificial intelligence? The boundaries are becoming increasingly blurred! In addition to the speed at which they perform tasks, machine learning models stand out for their complexity in processing information, yielding results that are increasingly faithful to human natural language.

Large Language Models (LLMs) can be defined as machine learning models capable of performing various tasks within the realm of language. They are constructed with a focus on understanding and generating texts.

The term "large" in LLM lives up to the fact of being a model trained with a vast amount of data, which leads to the capacity to relate multiple aspects and patterns of language.

What is the utility and distinguishing feature of LLM?

Okay, text generation by artificial intelligence is not the latest innovation, so what sets LLM apart? LLM surprises precisely because of its ability to generate highly coherent, grammatically correct, and cognitively convincing texts.

LLMs can perform tasks such as language translation, creating complete articles from zero, summarizing documents, help with virtual assistance, and even coding. The differentiator is its ability to interpret multiple functions within the same model.

The success of chatbots

Chatbots, such as ChatGPT and Bard, are examples of artificial intelligence technologies that utilize LLMs. These interfaces have gained much popularity in recent years by providing enhanced virtual assistance to users.

They can handle tasks ranging from administrative activities like creating lists, organizing spreadsheets, performing calculations, scheduling meetings, and formatting texts to more creative tasks like offering travel tips and even composing poems!

ChatGPT is an interface launched in 2022 by OpenAI. It uses the GPT-3.5 language models in its free version and GPT-4.0 in its paid version.

Bard is an interface developed by Google and launched in 2023. Its creation was based on the Language Model for Dialogue Applications (LaMDA) and later incorporated the Path Language Model (PaLM 2), capable of providing more precise responses. As it is still in the testing phase, this interface only works in the browser and does not yet have app versions.

Ethical challenges of artificial intelligence

AI applications can bring many opportunities to businesses, but there are ethical issues regarding the use of AI, as LLMs, that you should pay attention to. The main points of concern are:

Political and Social Sensitivity: The training of LLM doesn't consider social sensitivity, which means it can generate texts with some degree of prejudice, political incorrectness or even unethical content. Machine learning lacks real-world discernment, no matter how good the training is and how large the number of parameters, they don’t have social sensible filters as humans.

Fake News: Guarantee safe information is a point of concern using LLMs. Data training doesn't include fact-checking. There is no commitment of the data to truth, which can lead to outdated content, superficial interpretations, or even worse, the generation of fake news, or malicious and defamatory texts. 

Data Security: Another relevant point of concern is data security. The training cannot distinguish between what is confidential or not, so there needs to be a review of the data shared with the model, because LLMs should only feed on public data and trustable sources.

To address these dilemmas, the working has been doing to revise the guidelines for model input and output. Data scientists and AI experts are already studying techniques for fine-tuning and improving the understanding and precision of these systems.

How is an LLM built?

Large Language Models are trained with a large number of parameters, reaching billions. It is a massive algorithm that combines machine learning (the algorithm learns the rules on their own from the data they input) and deep learning technologies (high-level algorithms that mimic the neural human brain). The image below can help to understand:

The first step of training is to identify the dataset, which in LLM is substantial. After training, the layers of this large neural network are configured (several non-linear processing layers that simulate the way neurons think). Then supervised learning is used to produce relevant information from the provided data and, last but not least, performance tests on the model are conducted.

With this volume of data from the internet from various sources (articles, books, websites, videos), the model processes it to predict sequences of words that generate convincing text. It analyzes the text and predicts the next word, much like how people can "intuit" phrases in a conversation or while reading a text. However, LLMs do not possess cognitive intelligence. They only provide results based on what is previously provided to them.

Training language models requires specific technical knowledge, specialized software, and significant investments. According to a technical overview of OpenAI's GPT-3 language model, the cost of AI is increasing exponentially.

How to write relevant text inputs for LLMs?

Since LLMs do not possess their own "intelligence," the most effective way to enhance the accuracy of results is to use the right prompts when interacting with this network.

With each new command input, the model expands its database, always aiming to provide a more precise response. The more contextual the input command, the better the obtained response. Here's an example:

The technologies of Large Language Models point towards a revolution in language-related services. Will it become even more challenging each year to distinguish what was produced by a human or by artificial intelligence? 

In Bureau Work Translation Services, we believe that technologies are allies, not competitors. The key is to understand and study the tools to work for the benefit of as many people as possible.

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