81–90 of 173 posts
It's almost as if we trained LLMs on text produced by people.
I love the posters that make fun of those corporate motivational posters. My favorite is: No one is as dumb as all of us. And they trained their PI* on that giant turd pile. * Pseudo Intelligence
Wouldn’t the same outcome be achieved much more simply by giving LLMs a two choices (colors, numbers, whatever), asking “pick one” and assessing the results in the same way?
Is my understanding wrong that LLMs are trained to emulate observed human behavior in their training data? From that follows that LLMs fit to produce all kinds of human biases. Like preferring the first choice out of many, and the last our of many (primacy biases). Funnily the LLM might replicate the biases slightly wrong and by doing so produce new derived biases.
In most cases, The LLM itself is a name-less and ego-less clockwork Document-Maker-Bigger. It is being run against a hidden theater-play script. The "AI assistant" (of whatever brand-name) is a fictional character seeded into the script, and the human unwittingly provides lines for a "User" character to "speak". Fresh lines for the other character are parsed and "acted out" by conventional computer code.
That character is "helpful and kind and patient" in much the same way way that another character named Dracula is a "devious bloodsucker". Even when form is really good, it isn't quite the same as substance.
The author/character difference may seem subtle, but I believe it's important: We are not training LLMs to be people we like, we are training them to emit text describing characters and lines that we like. It also helps in understanding prompt injection and "hallucinations", which are both much closer to mandatory features than bugs.
Earlier quoted context omitted.
Why is that ? Whenever I’m giving examples I almost always use 7, something ending in a 7 or something in the 70s
1 and 10 are on the boundary, that's not random so those are out. 5 is exactly halfway, that's not random enough either, that's out. 2, 4, 6, 8 are even and even numbers are round and friendly and comfortable, those are out too. 9 feels too close to the boundary, it's out. That leaves 3 and 7, and 7 is more than 3 so it's got more room for randomness in it right? Therefore 7 is the most random number between 1 and 10…
> ...a lot of the safeguards and policy we have to manage humans own unreliability may serve us well in managing the unreliability of AI systems too. It seems like an incredibly bad outcome if we accept "AI" that's fundamentally flawed in a way similar to if not worse than humans and try to work around it rather than relegating it to unimportant tasks while we work towards a standard of intelligence we'd otherwise ex…
> ...a lot of the safeguards and policy we have to manage humans own unreliability may serve us well in managing the unreliability of AI systems too. It seems like an incredibly bad outcome if we accept "AI" that's fundamentally flawed in a way similar to if not worse than humans and try to work around it rather than relegating it to unimportant tasks while we work towards a standard of intelligence we'd otherwise ex…
Earlier quoted context omitted.
The theory I've heard is that the more prime a number is, the more random it feels. 13 feels more awkward and weird, and it doesn't come up naturally as often as 2 or 3 do in everyday life. It's rare, so it must be more random! I'll give you the most random number I can think of! People tend to avoid extremes, too. If you ask for a number between 1 and 10, people tend to pick something in the middle. Somehow, the ord…
Okay, this is a nitpick, but I don't think ordinal can be used in that way. "Somehow, the ordinal values of the range seem less likely". I'd probably go with extremes of the range? Or endpoints?
Earlier quoted context omitted.
LLMs don't emulate human behavior. They spit out chunks of words in an order that parrots some of their training data.
Correct me if I'm wrong, but I feel like we're splitting hairs here. > spits out chunks of words in an order that parrots some of their training data. So, if the data was created by humans then how is that different from "emulating human behavior?" Genuinely curious as this is my rough interpretation as well.
I know this is only adjacent to OP’s point, but I do find it somewhat ironic that it is easy to find people who are just as unreliable and incompetent at answering questions correctly as a 7b model, but also a lot less knowledgeable. Also, often less capable of carrying on a decent conversation. I’ve noticed an periconcious urge when talking to people to judge them against various models and quants, or to decide they…
A decent conversation about what?
Is my understanding wrong that LLMs are trained to emulate observed human behavior in their training data? From that follows that LLMs fit to produce all kinds of human biases. Like preferring the first choice out of many, and the last our of many (primacy biases). Funnily the LLM might replicate the biases slightly wrong and by doing so produce new derived biases.
Not only that if future AI distrusts humanity it is because history, literature and fiction is full of such scenarios and AI will learn those patterns and associated emotions from those texts. Humanity together will be responsible for creating a monster (if that scenario happens).
Together? It would be, 1. AI programmers, 2. AI techbros and a distant 3. AI fiction/history/literature. Foo who never used the internet: not responsible. Bar who posted pictures on Facebook: not responsible. Baz who wrote machine learning, limited dataset algorithms (webmd): not responsible. Etc.