Understanding Large Language Models and Their Role in AI

Large language models (LLMs) are the foundational technology behind popular AI chatbots such as ChatGPT, Google Gemini, and Microsoft Copilot. While these chatbots deliver impressive conversational results, they do not truly understand language as humans do. Instead, they function as interfaces to LLMs, which are trained to predict the next word in a sequence by analyzing vast amounts of text data.

Think of a language model as a sophisticated soothsayer for words. It predicts what comes next based on previous words, much like autocomplete in texting. Large language models are distinguished by their size, often containing billions of parameters—variables within neural networks that help process and generate language.

LLMs learn through deep learning, a process akin to teaching a child by example. They ingest enormous libraries of text—books, articles, code, and social media posts—to understand language patterns and nuances. This training involves breaking text into smaller units called tokens, enabling the model to map relationships between words and continuously refine its predictions.

However, the training data and methods have sparked controversy, with lawsuits alleging unauthorized use of copyrighted materials. Despite this, LLMs process trillions of tokens, learning statistical relationships rather than true meaning. They also improve through reinforcement learning from human feedback, which helps refine their responses based on preferences.

LLMs excel at predicting the next word in a sequence by leveraging stored patterns. For example, given the phrase "I went sailing on the deep blue...," an LLM can accurately predict "sea" based on contextual clues. Their vast parameters enable them to generate coherent, fluid paragraphs that mimic human language.

There are various types of language models, including smaller models designed to run on devices with limited computing power, reasoning models that reveal their thought process, and open-source or open-weights models that offer transparency and customization. Some models are multimodal, trained on text, images, video, and audio.

Despite their strengths, LLMs have notable weaknesses. They can hallucinate, generating plausible but false information, struggle with novel or unique problems like complex math, and lack true understanding of current events due to static training data. They also cannot plan or make decisions like humans, limiting their use in dynamic scenarios.

To address some limitations, LLMs are increasingly integrated with search engines, allowing them to access up-to-date information and improve response accuracy. However, this integration raises challenges around verifying the reliability of web sources and mitigating the risk of amplifying misinformation.

Understanding how large language models operate is essential as AI becomes more embedded in daily life. They represent a powerful tool for generating human-like text and enabling new applications, but recognizing their limitations is key to using them responsibly and effectively.