[devjab]

> The model takes the prompt. The first following output token it chooses, for this specfic model, happens to be 'Left'. They shut down the prompt and prompt again. Of course the next output will be 'Left'.

I think what you say is correct, but isn’t this exactly the point the author is trying to make? You explain why it happens, but ultimately the result is still that the LLMs can’t do probability. At least not in the way the author presents it.

What I’m more interested in is what sort of consequences these debates and different understandings/views of LLMs will have on us in general. We monitor the usage of co-pilot in our organisation and it’s been interesting to watch how a lot of employees have started promoting the same task to multiple “agents” simultaneously. One person tends to always run at least five at once. So naturally we dug a little into why people were doing it, and, they are doing it because “it’s the fastest way to get something useful”. Which is probably not too surprising to a lot of you, but it was sort of hilarious to follow along and see how two promoted “agents” gave completely conflicting answers.

I know LLMs will probably get better at being lucky, but some of these tasks… honestly we had a couple of our juniors who were very you’re of always going GPT of co-pilot first, simply look at the official documentation and we timed them, and it was so much faster for them to not use LLMs. This isn’t me saying LLMs suck, we deliberately did it to make sure the juniors in question had an “oh” moment. But it’s interesting to see how quickly our employees have adopted LLMs.

[lukeschlather]

Yeah increasingly I only use LLMs for very simple things, and still I think at least 25% of the time I end up reading the doc I was trying to avoid anyway.