What are large language models?


Thanks to ChatGPT’s release, the modern concept of AI was launched into mainstream recognition. Just two months after it had gone public, ChatGPT had around 5 million visits daily. Today, everyone’s talking (and arguing) about AI’s capabilities.

ChatGPT is a large language model (LLM), representing a significant advance in AI. Even though the average person may understand that AI is designed to mimic human behavior – whether beating a grandmaster in chess, creating art, or answering customer queries – they may not realize the complexity of the technology that makes this possible.

Let’s dive deep into what LLMs are and how they shape our interactions with AI technology.

Understanding Large Language Models

A large language model is a form of AI that leverages machine learning algorithms to understand, generate, and respond in natural human language.

As per the name, LLMs use enormous amounts of data, often from the internet, to learn and understand patterns in our language – the linguistic structures, colloquialisms, nuances, and even the emotional context of words. They can then synthetically reproduce this language in a way that appears quite human.

Looking Back at How Large Language Models Developed

The inception of LLMs can be traced back to the early 1960s with the development of Eliza, one of the first chatbots ever created. Although relatively rudimentary, Eliza was groundbreaking at the time as it simulated conversation by recognizing and rehashing certain predetermined phrases.

However, it wasn’t until the deep learning boom of the 2010s that these models began to use massive amounts of data and computing power to achieve far greater fluency and comprehension.

MongoDB’s look at large language models explored the introduction of transformer architecture in 2017, which marked a turning point in the history of AI. Transformer models read entire sentences at once, enabling them to better understand the overall context within human language.

Transformers set the groundwork that eventually enabled the development of AI, like OpenAI’s GPT, one of the most powerful LLMs today.

How Large Language Models Work

In essence, large language models work by predicting the likelihood of a word following a given set of words. Using probability and statistics, an AI model like GPT-3 can predict all the possible combinations of words that could follow, scaling it according to their potential occurrence within its trained data.

Whenever the model anticipates the next word in a sentence, it doesn’t just look at the previous couple of words. Instead, it considers the entire sequence it has seen so far. This is known as the “context window” of a language model. The larger the context window, the more effective and accurate the model’s predictions and outcomes.

Internally, LLMs, like transformer models, use ‘attention mechanisms’ to discern which input parts are important when generating an output. This helps model intricate relationships between words and their position in the sentence.

Use Cases of Large Language Models

LLMs are finding immense utility in varied fields due to their ability to understand natural language.

As detailed in RoboticsBiz’s previous posts about chatbots, one of the biggest use cases of LLMs is in customer service. Chatbots powered with LLMs can handle customer inquiries and complaints and deliver personalized service. Enterprises are also using LLMs to sift through enormous volumes of data and extract useful insights in market research, social sentiment analysis, and more.

In education, LLMs are being used to create intelligent tutoring systems that can provide personalized feedback and instruction to students. Our article on ‘AI and Robotics in Education’ talks more about this. Meanwhile, medical practitioners leverage LLMs to extract and synthesize information from medical literature and patient records, aiding in diagnostics and treatment plans.

Advanced AI models like GPT-4 also allow developers to create applications using natural language, opening the coding field to a wider range of people.

The Limitations of Large Language Models

Large language models have immense potential but are not without their disadvantages. Current LLMs are inherently limited by the data quality they are trained on. The AI will echo this bias in its outputs if the data is biased or inaccurate.

Additionally, while these models can mimic human-like conversation, they don’t truly understand language like humans do. Their responses are based on patterns in data rather than conscious understanding. This often results in incorrect or nonsensical outputs, even if the prose is grammatically correct.

Finally, the ethical implications and potential misuse of these powerful language models are ongoing research and debate topics. For instance, the unmonitored use of such technology might facilitate the diffusion of deepfake content or false information. A VOA report detailed the devastating results of a deepfake video scam wherein a Hong Kong company lost $26 million. Therefore, it is imperative to implement and adhere to stringent use guidelines.

Final Word

Giant strides have been made in developing AI technology over the last decade, with large language models leading the way. They represent an important milestone in the AI journey, and as technology progresses, they will continue to be refined and optimized. Grand View Research predicts the AI market size to grow to $1,811.8 billion by 2030.

Yet, as with any powerful technology, the ethical and security implications must also be carefully navigated. The opportunity to shape this outcome is among the many exciting challenges facing AI enthusiasts and professionals today.