Demystifying LLMs
Language models have become a cornerstone of modern artificial intelligence, not only revolutionizing the way we interact with technology, but also the way in which we interact with each other. Think chatbots, virtual assistants and even speech translators.
But contrary to widespread belief, the technology isn’t completely new! In fact, you could argue that it dates all the way back to the nineteen sixties when MIT professor Joseph Weizenbaum simulated conversations using pattern matching and substitution with his ELIZA programme.
At the time, Eliza gave the illusion of intelligent conversation, but really had no conceptual understanding of what was being said by either party.?
So, what has changed??
Well today, language models are trained on massive amounts of data, typically billions of words from a variety of sources including books, articles, social media posts and more. They are also trained with specific parameters that help contextualise and interpret meaning so that they can formulate a tone in their responses.
To give you some context.
Needless to say, Davinci can handle almost any natural language task thrown at it, creating coherent and creative text with fluency, consistency, and diversity.?
So, what do they do?
At their core, language models have always been about predicting the next word in each sentence, first using dictionaries, then recurrent neural networks and more recently transformers!?
Transformers introduce the concept of attention meaning that for every word entered, the model learns how it is related to every other word in the text. Therefore, by processing and understanding the patterns within the language, the model can generate meaningful and contextually appropriate responses.
Because of that, LLMs are often used to automate customer support interactions, accelerating the time to solution without having to wait for a support agent. They are also great with content creation, such as authoring articles and blog posts (But not this one!)?
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But my biggest use for LLMs in is in language translation activities, in fact if you follow me on LinkedIn, you will have seen my posts about my Thailand farm project, largely successful due to the translation capabilities of modern AI.?
So, what’s that catch with LLMs?
Well, they are great but as with anything, it’s not all sunshine and rainbows. At times, LLMs are in fact the world’s best liars! In technical terms this is called hallucinations and means that the LLMs will produce a convincing output which in either some or all parts is completely wrong.
That’s why it is important to check the facts and not follow the content produced blindly.?
Secondly, they can also amplify bias, especially in gender and racial views. Remember they are only as good as the data they have been trained on and historically, humans haven’t always been the best.
What I will say though is that they are getting much better, thanks to the responsible AI practices that have been employed worldwide.?
So how do we communicate with LLMs??
Now I won’t spend long on this section as I can imagine that most of you at this point have used ChatGPT or one of the many other GPT powered search engines.?
But I will reinforce the concepts of good prompting.
You see effective use of LLMs requires creativity and attention to detail. It involves selecting the right words, phrases, and examples to generate high-quality outputs. You are only going to get out what you put in, so be elaborate, give fitting examples, define the audience, persona, genre, and tone.
In conclusion, large language models represent a paradigm shift in how machines understand and interact with human language. Their ability to generate contextually appropriate and natural-sounding responses opens countless possibilities for businesses across different industries.?
By incorporating large language models into your AI workloads, you can streamline processes, deliver exceptional customer experiences, and stay ahead in an increasingly competitive landscape.