@earthquake You’re correct that projecting agency to the LLM is problematic, but in doing so, we get better quality results. I’ve argued that we need new words for LLMs instead of “think,” “understand,” “learn,” etc. We’re anthropomorphizing them and this makes people less critical and gradually shifts their attitudes in incorrect directions.
Unfortunately, I don’t think we’ll ever develop new words which more accurately reflect what is going on.
Seriously, what kind of reply is this, you ignore everything I said except the literal last thing, and even then it’s weasel words. “Using agential language for LLMs is wrong, but it works.”
Yes, Curtis, prompting the LLM with language more similar to its training data results in more plausible text prediction in the output, why is that? Because it’s more natural, there’s not a lot of training data on querying a program on its inner workings, so the response is less like natural language.
But you’re not actually getting any insight. You’re just improving the verisimilitude of the text prediction.
Got it, because the output you saw from doing this looks really really plausible. Disappointing, but what other answer could it have been?
Here’s a story for you: a scientist cannot get his papers published. In frustration, he complains to his co-worker, “I have detailed charts on the different type and amount of offerings to the idol, and the correlations to results on prayers answered. I think this is a really valuable contribution to understanding how to beseech the gods for intervention in our lives, this will help people! Why won’t they publish my work?”
His co-worker replies, “Certainly! As a large language model I can see how that would be a frustrating experience. Here are five common reasons that research papers are rejected for publication.”
@earthquake You’re correct that projecting agency to the LLM is problematic, but in doing so, we get better quality results. I’ve argued that we need new words for LLMs instead of “think,” “understand,” “learn,” etc. We’re anthropomorphizing them and this makes people less critical and gradually shifts their attitudes in incorrect directions.
Unfortunately, I don’t think we’ll ever develop new words which more accurately reflect what is going on.
Seriously, what kind of reply is this, you ignore everything I said except the literal last thing, and even then it’s weasel words. “Using agential language for LLMs is wrong, but it works.”
Yes, Curtis, prompting the LLM with language more similar to its training data results in more plausible text prediction in the output, why is that? Because it’s more natural, there’s not a lot of training data on querying a program on its inner workings, so the response is less like natural language.
But you’re not actually getting any insight. You’re just improving the verisimilitude of the text prediction.
Got it, because the output you saw from doing this looks really really plausible. Disappointing, but what other answer could it have been?
Here’s a story for you: a scientist cannot get his papers published. In frustration, he complains to his co-worker, “I have detailed charts on the different type and amount of offerings to the idol, and the correlations to results on prayers answered. I think this is a really valuable contribution to understanding how to beseech the gods for intervention in our lives, this will help people! Why won’t they publish my work?”
His co-worker replies, “Certainly! As a large language model I can see how that would be a frustrating experience. Here are five common reasons that research papers are rejected for publication.”