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Kofitech’s short guide to LLMs

8/13/2024 | nmb@konfitech.com

A large language model or a LLM is a subsection of the greater artificial intelligence topic. For those looking for an in-depth guide to LLM, it’s mainly concerned with analysing natural language data to generate responses to prompts.

Their algorithms are structured in way that they analyse human language and the patterns and structures. Which allows them to generate responses that makes sense based on what the input prompt is. For a guide to LLM, common use cases include translation, chat-bots, etc.

LLMs are not a new thing. It has been around for years, but not been well-known to the public until now. With arguably the first projects starting in the 1990s or early 2000s. However, the release of ChatGPT has opened access to the biggest and best LLM models in the world to anyone with internet, causing widespread popularity.

The sudden progress now in the 2020s is arguably down to three factors:

  • High quality data with high significance for predictability.
    • The better the data, the less computational power you need. The algorithm gets better for each high-quality data point, as outlined in any thorough guide to LLM.
  • Humans-in-the-loop providing feedback to the LLM.
    • Humans speak and talk all day; we know what a natural sounding speech pattern is supposed to be. So instead of leaving the computer to guess we can provide great guidance.
  • Growing computational power.
    • Even if you have a terrible car putting a better engine in it will make it run faster and better, much like improving your system with an advanced guide to LLM.

With the emergence of very usable LLM models, what are some of the most common use cases out there.

As mentioned above LLMs are used to generate and consider natural language. So, we see use cases emerging in often non-domain specific natural language tasks. Meaning that the cases where it is used it is not considering deep domain level knowledge, but an analysis of natural language and what an appropriate response could be, which you can often find in any comprehensive guide to LLM.

These are things like Chatbots, code generation, sentiment analysis, text classification, clustering of text, language translation, summarization, paraphrasing, content generation.

The LLM knows the general structure of your input and knows what common patterns and structures are given based on that input. Meaning they are not great at facts, like who won the Superbowl, but they know how to use language.

Therefore, when applying LLMs to your enterprise or business, you have two great opportunities to use it.

  1. Use what is out there and pre-built.

You can make use of the great LLMs out there through their APIs, this way your own applications can directly interface with the LLMs and generate responses to your input. This guide to LLM approach is highly practical and efficient.

It is a very low resource intense way of applying LLMs and can be done quite fast without extensive research into constructing it yourself.

However using a proprietary service, you are giving up control in information sharing and processing of the information you are feeding it. Many models are so-called black boxes meaning they do not know what it does with the data, and you are also having to transmit the data to third parties all the time.

  1. Built your own.

There are many super viable great open-source opportunities within the LLM space. Some suppliers like Hugging Face builds thousands of different open-source LLMs that you can apply to your use cases. For those willing to venture, a guide to LLM can be invaluable.

This gives you a lot of control over what the LLM does, giving you control over fine tuning to data. Giving them domain specific training, meaning that it is less likely to hallucinate and give fake facts. Making it more useful to your domain of business.

The only drawback here being the performance of the LLM generally is lower than the proprietary services, since a lot of the time you must train and run it yourself.

I hope that was useful, if you want to learn more about LLMs, use-cases for your business, or you already have an idea you want to implement do not hesitate to reach out to us at: https://www.konfitech.com/contact-us

Or learn more about what we can do in Data & AI here: https://www.konfitech.com/data-and-ai

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