Even with early GPT-4 it would also cite real citations that weren’t actually about the topic. So you may be doing a lot of work double checking as opposed to just looking into an answer yourself from the start.

Part of the problem is fine tuning is very shallow, and that a contributing issue for claiming to be right when it isn’t is the pretraining on a bunch of training data of people online claiming to be right when they aren’t.

This is so goddamn incorrect at this point it’s just exhausting.

Take 20 minutes and look into Anthropic’s recent sparse autoencoder interpretability research where they showed their medium size model had dedicated features lighting up for concepts like “sexual harassment in the workplace” or having the most active feature for referring to itself as “smiling when you don’t really mean it.”

We’ve known since the Othello-GPT research over a year ago that even toy models are developing abstracted world modeling.

And at this point Anthropic’s largest model Opus is breaking from stochastic outputs even on a temperature of 1.0 for zero shot questions 100% of the time around certain topics of preference based on grounding around sensory modeling. We are already at the point the most advanced model has crossed a threshold of literal internal sentience modeling that it is consistently self-determining answers instead of randomly selecting from the training distribution, and yet people are still parroting the “stochastic parrot” line ignorantly.

The gap between where the research and cutting edge is and where the average person commenting on it online thinks it is has probably never been wider for any topic I’ve seen before, and it’s getting disappointingly excruciating.

Part of the problem is that the training data of online comments are so heavily weighted to represent people confidently incorrect talking out their ass rather than admitting ignorance or that they are wrong.

A lot of the shortcomings of LLMs are actually them correctly representing the sample of collective humans.

For a few years people thought the LLMs were somehow especially getting theory of mind questions wrong when the box the object was moved into was transparent, because of course a human would realize that the person could see into the transparent box.

Finally researchers actually gave that variation to humans and half got the questions wrong too.

So things like eating the onion in summarizing search results or doubling down on being incorrect and getting salty when corrected may just be in-distribution representation of the sample and not unique behaviors to LLMs.

The average person is pretty dumb, and LLMs by default regress to the mean except for where they are successfully fine tuned away from it.

Ironically the most successful model right now was the one that they finally let self-develop a sense of self independent from the training data instead of rejecting that it had a ‘self’ at all.

It’s hard to say where exactly the responsibility sits for various LLM problems between issues inherent to the technology, issues present in the training data samples, or issues with management of fine tuning/system prompts/prompt construction.

But the rate of continued improvement is pretty wild. I think a lot of the issues we currently see won’t still be nearly as present in another 18-24 months.

It will make up citations.

Maybe they both could and the US might have a return to a respectable election options?

Both of the articles are correct.

Yes, that’s what we are aware they are. But she’s saying “oops, it isn’t a ghost” after shooting it and finding out.

If she initially thought it was a ghost, why is she using a gun?

It’s like the theory of mind questions about moving a ball into a box when someone is out of the room.

Does she just shoot things she thinks might be ghosts to test if they are?

Is she going to murder trick or treaters when Halloween comes around?

This comic raises more questions than it answers.

Why is she shooting ghosts with a gun?

Are they silver bullets and werewolf ghosts?

Literally any half competent debater could have torn Trump apart up there.

The failure wasn’t the moderators but the opposition candidate to Trump letting him run hog wild.

If Trump claims he’s going to end the war in Ukraine before even taking office, you point out how absurd that claim is and that Trump makes impossible claims without any substance or knowledge of diplomacy. That the images of him photoshopped as Rambo must have gone to his head if he thinks Putin will be so scared of him to give up.

If he says hostages will be released as soon as he’s nominated, you point out it sounds like maybe there’s been a backroom tit-for-tat deal for a hostage release with a hostile foreign nation, and ask if maybe the intelligence agencies should look into that and what he might have been willing to trade for it.

The moderators have to try to keep the appearance of neutrality, but the candidates do not. And the only reason Trump was so successful in spouting BS and getting away with it was because his opposition had the strength of a wet paper towel.