Have you ever used ChatGPT or Google Gemini for a task or project? These are examples of large language models. I often find myself using these tools for brainstorming or simple questions I can’t bother to google. This week I decided to explore large language models (LLMs) because I wanted to gain a better understanding of these new tools creating a buzz around the entire world. To get a good understanding of this topic, I explored the article What Businesses Should Know About Large Language Models (LLMs) by Victoria Shashkina (https://itrexgroup.com/blog/what-are-large-language-models-llms/) In relevance to data science, these models are powerful tools that allow users to interact with data through natural language prompts. Furthermore, these models are centered around using data to develop responses to prompts.

These are a couple LLMs you may have heard of: Google Gemini, ChatGPT, Llama, Claude.

Large language models (LLMs) are a type of artificial intelligence (AI) that are trained on massive amounts of text data. This data can include books, articles, code, and even social media posts. As a result, LLMs are able to communicate and generate human-like text in response to a wide range of prompts and questions.

LLMs are trained using a process called supervised learning. This involves feeding the LLM a large dataset of text and labels. The LLM then learns to identify patterns in the data and use those patterns to generate new text. As the model intakes more and more text, it forms meaning behind the words and relationships. To generate text, the model develops responses word-by-word, choosing the best possible next word based on the sentence it is currently forming. Through repeating this pattern, these models can answer very complex questions and follow direct orders. As such, models are only capable of what they have been trained on, so the more data involved within the development of the AI model, the more accurate.

In the industry, LLMs have a variety of possible usage:

• Customer service: LLMs can be used to provide customer service via chatbots or virtual assistants. This can help businesses save time and money.

• Content generation: LLMs are often used to generate content for websites, blogs, and social media. This could be pictures, descriptions, etc. Businesses use this technique to save time and money.

• Market research: LLMs can be used to analyze customer sentiment and identify trends. This is more data science related as LLMs can be used to analyze any general data for companies. This can help businesses make better decisions about their products and services.

There are some challenges to consider when using LLMs, though. This new technology may have many opportunities for new insights, however it definitely has some limitations. Because LLMs are solely based on data the models are trained on, they may produce biased or inaccurate results if the data is corrupt. Furthermore, there is an existing bias to overuse or underuse LLMs within the industry. Many employees see these models as threats to job security or they simply would rather solve issues without the help of computers. On the other hand, an overuse of this technology can negatively impact authenticity and produce unreliable results.

Overall, LLMs are a powerful tool that can be used to improve business operations. However, it is important to be aware of the challenges associated with LLMs before adopting them.

Personally, I believe LLMs to be pretty powerful and reliable. Although these models are still under daily development and require precise fine-tuning, their capabilities are constantly evolving and they have revolutionized the way work is handled. It’s also important to consider the potential impact of these models on society and the ethical implications about having such powerful tools readily available. Furthermore, I’ve used some of these models when coding new projects such as a Chatbot; these models are not just for direct usage, but also can be used for personal projects, showcasing the limitless capabilities of this new technology.