Key Learning Points:

  • LLMs (Large Language Models) are AI technologies that learn from vast amounts of text data to understand and generate natural human-like language.
  • LLMs use a mechanism called “Transformer” to grasp the flow and nuances of sentences, enabling them to produce appropriate responses.
  • While highly useful, LLMs also face challenges such as generating incorrect information or learning biased content from their training data.

Why can AI understand language now?

Think about the smartphone you use every day. The chat apps and search engines inside it are becoming more and more advanced, responding in ways that feel like you’re talking to a real person. Popular AI tools like ChatGPT and Gemini also rely on a core technology known as LLM—short for Large Language Model.

The name might sound complicated, but LLMs are actually playing an increasingly important role in our everyday lives.

What is an LLM? A gentle explanation of how it works

An LLM is essentially “an AI that has learned a huge amount of language.” As its Japanese name suggests—“Large-Scale Language Model”—it becomes capable of understanding and generating natural-sounding text by reading massive amounts of written material.

The way it works is by identifying patterns in the texts it has read and predicting what word is likely to come next. At the heart of this process is a structure called the “Transformer.” This mechanism excels at looking at the overall flow and meaning of a sentence, allowing the LLM to respond more intelligently and flexibly.

Where is it useful? And what challenges does it face?

Imagine asking an AI, “What’s the weather tomorrow?” An LLM doesn’t just look at individual words—it considers the time frame implied by “tomorrow,” understands your intent behind asking, and even takes into account the flow of the conversation. That’s why its responses feel so natural.

Thanks to this technology, we now receive support from AI in many areas—like writing emails or brainstorming ideas for stories or proposals.

However, it’s not all smooth sailing. Sometimes, AI generates information that sounds plausible but is actually incorrect—a phenomenon known as “hallucination.” Also, because LLMs learn from large datasets, if those datasets contain biased opinions or discriminatory expressions, there’s a risk that such biases may be absorbed unconsciously.

To address these issues, researchers are working hard to improve fairness and safety in how these models operate.

Toward more human-like conversations: The future of AI and language

So why do so many companies and researchers continue to focus on this field? It’s because language plays such a central role in human society. Everyday conversations, business reports, social media comments—our lives revolve around words. And LLMs are among the technologies coming closest to enabling machines to engage in truly human-like communication.

This technology will likely continue evolving and finding new applications. Behind this progress lies a long-standing question: “How can machines understand language like humans do?” Today, we’re getting closer than ever to answering that question.

In our next article, we’ll take a closer look at one of the most well-known models supporting this technology: GPT. Let’s explore together how it became so intelligent over time.

Glossary

LLM (Large Language Model): An AI model that learns from vast amounts of text data to understand and generate natural human-like language.

Transformer: An internal mechanism within AI that excels at understanding the overall flow and meaning of sentences. It enables advanced language processing capabilities.

Hallucination: A phenomenon where an AI generates information that appears accurate but is actually incorrect. Careful attention is needed when relying on such outputs.