We revisit the topic of Large Language Models (LLM), which is a critical subject for understanding the foundation of artificial intelligence (AI), and we examine it in more technical detail without exceeding a basic level of understanding.
Large Language Models (LLM) are advanced artificial intelligence systems created to understand and generate text in a manner that appears as natural and human as possible. Let’s explore together how these models function without delving into complicated technical details.
What are Large Language Models exactly? At its core, an LLM is an extremely sophisticated computer program that has been trained to process and generate language. The training of an LLM is done by exposing it to a vast amount of written text. By analyzing these texts, the model learns how sentences are structured, which words are often used together, how to form grammatically correct phrases, and much more about language.
How are these models trained? The training of an LLM is similar to the way a child learns a language. Just as a child listens when people talk and reads various materials, our model “reads” a multitude of written texts. The training process is supervised by algorithms that adjust the model’s “understanding” to improve how it responds and generates text. Whenever the model makes a mistake, the algorithm corrects these mistakes, and the model learns from these corrections.
Why are they called “large”? The word “large” in their name is not incidental. These models are “large” because they utilize millions or even billions of parameters. Parameters are like basic rules or variables that the model uses to decide how to respond to questions or generate text. The more parameters there are, the more precisely the model can be and capture more nuanced aspects of the language.
How does an LLM respond to our questions? When you ask a question to an LLM, it processes the query based on everything it has “learned” during its training. It searches its knowledge base for how it should respond, according to the data it has analyzed. Then, using the rules and parameters it has learned, it generates a response that hopes to be as close as possible to what a human might respond in that situation.
To deepen our understanding of how Large Language Models (LLM) function and their impact, we can begin by exploring in more detail how these are structured and what their continuous training entails. It is also important to understand their impact on society and current limitations.
Structure and Mechanics of an LLM. An LLM uses a structure called a “neural network”, which is inspired by how the human brain processes information. This network is made up of layers of nodes (or artificial neurons) that are interconnected. Each node receives input from previous nodes, processes the information, and sends output to the following nodes. This process is repeated through each layer of the network, allowing the model to learn complex and subtle relationships between words and phrases.
Continuous Training and Adaptation. The training of an LLM never really stops. Although the model becomes functional after an initial intensive learning period, it can be continuously improved and adjusted. This means that as new texts or information become available, the model can be “retrained” or adjusted to understand and reflect changes in language or current information. For example, new words or phrases that become popular can be integrated into the model’s learning.
Impact on Society. LLMs have a significant impact on many fields, from technology and education to business and entertainment. They can automate tasks that involve text, such as responding to customer inquiries, translating foreign languages, generating creative content, and even writing software code. This ability to manipulate and generate language in such varied ways opens up enormous opportunities for innovation but also raises important ethical questions.
Limitations and Ethical Challenges. Although LLMs are powerful tools, they are not without problems. A major limitation is that they can perpetuate or amplify the biases included in the training data. If the texts used for training contain stereotypes or errors, the model can learn and reproduce these issues. LLMs can generate information that seems plausible but may be partially or completely false, which can mislead. Additionally, they do not have consciousness or their own emotions – the responses generated are based strictly on the information and training they have received, not on personal or emotional experiences.
Conclusion. Large Language Models are powerful tools in the field of artificial intelligence, which help us automate and improve various tasks related to language processing. They offer us the ability to interact with machines in a natural way, using the language we speak. Through training based on a massive amount of text, these models have become capable of understanding and generating language in a way that was inconceivable in the past. LLMs transform the way we interact with technology, providing new methods of communicating with machines and automating processes that previously required extensive human intervention. However, as these technologies become more integrated into our daily lives, it is crucial to continue to evaluate and improve them, taking into account the ethical and social implications of their use. By deeply understanding their functionality and limitations, we can use these tools responsibly and effectively, maximizing their benefits for society while minimizing the associated risks.
(Article generated and adapted by CorpQuants with ChatGPT)