Musings and Thoughts

Since my last post I’ve been thinking about why LLMs work as well or poorly as they do and I think I’ve come up with a working hypothesis. Admittedly, it’s probably obvious, but hey! I never said I was quick!

What Were These Systems Trained On?

Programming

So, in the case of programming, these LLM systems have been trained on things like code in GitHub or posts on Stack Overflow. What are these posts typically? I would assume these posts are typically amateur programmers asking entry level questions more often than not. Further, the questions will likely be for smaller systems or short code snippets and not entire applications. Given that sort of training bias, I would expect for programming that an LLM would be well versed in simple programming tasks and less so in more advanced tasks. And I think, in that line of reasoning, it would be safe to expect the kind of output seen from the reports I reviewed in my last post.

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Aritificial Intelligence Review

Recently I decided to dive into the literature on the current-ish state of Aritificial Intelligence (AI), and more specifically large language models (LLM), to see what the current potential pitfalls may be. I was interested in the accuracy of outputs from LLMs and what effects that accuracy, or lack thereof, may result in. In my review I’ve seen a few interesting things that I’m going to just summarize here – I’m not a writer or journalist. I’m just a software developer, so bear with me.

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I am back home on Gentoo Linux. it feels good.