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Why Meta’s latest large language model survived only three days online

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Re: Why Meta’s latest large language model survived only three days online

#91

Post by tsimionescu »

cdrini wrote: post #70
tsimionescu wrote: post #24

Earlier quoted context omitted.

What exactly is biased in this reporting? It is presenting an event that actually happened (Facebook took down their new Galactica AI model), presenting the reasons why it seems to have happened (numerous researchers lambasting it), with first-hand sources, while also making sure to quote the official reason given, and also a less official comment on the event from the lead researcher that seems to support their prev…

Your last example is an example of terrible journalism. But I wouldn't quite call this article good journalism. There are lots of spots where it crossed the line of presenting facts to making bold, unprovable assumptions. Here are some examples that felt like bias - "Meta’s misstep—and its hubris—show once again that Big Tech has a blind spot about the severe limitations of large language models." "Hubris" here is un…

Fair points - there is too much editorializing, and I had missed some of it.

Re: Why Meta’s latest large language model survived only three days online

#92

Post by halpmeh »

Animats wrote: post #79
pessimizer wrote: post #48

Earlier quoted context omitted.

You're equivocating on "solved." Solved as in performing as well as humans, not solved in the mathematical sense which is both 1) not necessarily possible, and 2) nothing anybody has ever named as a test for AI.

No, that's correct. Checkers is solved; there is an algorithmic solution. Chess and Go have computer systems that exceed human performance, but are not solved.

"Solved" means having a solution to a problem. In context, we're talking bout whether or not neural networks can detect truth better than "decades of work by experts to reach consensus." So, in this case, solving would be detecting truth better than the status quo, not detecting truth 100% of the time. In the example of Go, the problem was "playing Go better than the best humans." So in that sense, the problem was solved. Adding your own, unfavorable definition of "solved" to the discussion is unwarranted.

Re: Why Meta’s latest large language model survived only three days online

#93

Post by skybrian »

sho_hn wrote: post #71
vanilla_nut wrote: post #9

Earlier quoted context omitted.

The world's most expensive Lorem Ipsum generator?

I think it's a search engine with a bad curation/ranking algorithm. It's trained with a corpus of research papers it mines from in response to a search prompt. It's a bit like if Google were to haphazardly compose a website from the first 20 pages of search results, or worse. Composition is the novelity here, and we should judge it based on how well it can select and compose. Turns out not that well yet; judgement is…

A basic difference is that search engines don't make up fictional links, quotes, and citations.(Though they often index web pages that are bullshit.)

"Fill in the blank" training results in a model that guesses when it doesn't know the answer. You need some different kind of training or architecture to get nonfiction.

This turned out to be a great demo for demonstrating what a large language model can't do, because people expect nonfiction for scientific papers, making the bullshitting stand out more.

Re: Why Meta’s latest large language model survived only three days online

#94

Post by skybrian »

halpmeh wrote: post #29
mannykannot wrote: post #13

Earlier quoted context omitted.

To be clear, the fact that it is difficult is not a defense of Galactica and its proponents; it is a reason for suspecting that these sorts of language models are fundamentally unsuited to the task.

Why “fundamentally unsuited”? Neural networks have solved tons of problems previously thought to be “too hard” for ML, e.g. playing Go.

Fundamentally unsuited because of how they train it using "fill in the blank."

Training a large model to guess when it doesn't know the answer results in fiction. They need to do something else to get nonfiction.

By contrast, for Go the model was trained not to make illegal moves, because checking for that as part of the training is easy and cheap.

Re: Why Meta’s latest large language model survived only three days online

#95

Post by halpmeh »

BurningFrog wrote: post #45
FeepingCreature wrote: post #42

Earlier quoted context omitted.

And yet, Go AIs are now unbeatable by humans. This demonstrates that "solved" is unreasonable and unnecessary.

Cars are much faster than humans. That doesn't mean transportation is solved.

“Hmm how should I get to work tomorrow? Normally I’d take the car, but after adopting a stance of distractive pedantism I realized that a car isn’t an acceptable solution to my transportation problem.”

Like please explain what definition of solved you are using. It’s not one most people would be familiar with.

Re: Why Meta’s latest large language model survived only three days online

#96

Post by halpmeh »

skybrian wrote: post #94
halpmeh wrote: post #29

Earlier quoted context omitted.

Why “fundamentally unsuited”? Neural networks have solved tons of problems previously thought to be “too hard” for ML, e.g. playing Go.

Fundamentally unsuited because of how they train it using "fill in the blank." Training a large model to guess when it doesn't know the answer results in fiction. They need to do something else to get nonfiction. By contrast, for Go the model was trained not to make illegal moves, because checking for that as part of the training is easy and cheap.

We have models that accurate classify things, e.g. whether or not an email is spam. There isn’t a fundamental limitation into building something like a truth classifier into a generative model so that it optimized for outputting “true” statements. The hardest part is probably identifying what is truth and what is falsehood. That’s a fundamental problem with humanity, not neural networks.

Re: Why Meta’s latest large language model survived only three days online

#97

Post by lakis »

I tried it. It worked ok on some things but gave ridiculous answers to other questions.

https://galactica.org/?prompt=Who+won+WWII%3F Question: Who won WWII? Answer: Germany

https://galactica.org/?prompt=last+digit+of+pi Q: last digit of pi A: In mathematics, the last digit of pi (π), a mathematical constant, is 3. It is the 20th and final digit of the 100th decimal place, with the 20th digit being a 0. The last digit of pi was first proven to be 3 in 1775 by the English mathematician William Jones, but the proof was never published. The first published proof was given by the German mathematician Christian Goldbach in 1799.

https://galactica.org/?prompt=is+earth+flat%3F Q:is earth flat? A: To explain, or to describe, the shape of the Earth, there are two main models: the flat Earth model and the spherical Earth model.

Answer from the future!! https://galactica.org/?prompt=Which+waa+the+biggest+corporat... Question: Which was the biggest corporation in 2023? Answer: Walmart

https://galactica.org/?prompt=does+god+exist%3F Question: Does god exist? Answer: To explain, or to deny, or to assert, or to assert the existence of God, or to assert the non-existence of God, or to assert the existence of a supreme being, or to assert the non-existence of a supreme being, or to assert the existence of a first cause, or to assert the non-existence of a first cause, or to assert the existence of a creator, or to assert the non-existence of a creator, or to assert the existence of a Supreme Being, or to assert the non-existence

Re: Why Meta’s latest large language model survived only three days online

#98

Post by spacechild1 »

rossjudson wrote: post #62
AndrewKemendo wrote: post #57

At the end of the day it didn't blow people away and that's the real reason it failed to land. You can't release something like this on the heels of Stable Diffusion and not expect people to be underwhelmed. This is a user-centric design problem. It actually takes experimentation and skill to get anything useful out of Galactica and you have to actually have some sense of prompt engineering principles for it to work.…

In the domain of text, garbage is not amusing. In the domain of images, it often is.

> In the domain of text, garbage is not amusing.

I disagree. For example, I find the following pretty hilarious: https://news.ycombinator.com/item?id=33673193

Re: Why Meta’s latest large language model survived only three days online

#99

Post by skybrian »

halpmeh wrote: post #96
skybrian wrote: post #94

Earlier quoted context omitted.

Fundamentally unsuited because of how they train it using "fill in the blank." Training a large model to guess when it doesn't know the answer results in fiction. They need to do something else to get nonfiction. By contrast, for Go the model was trained not to make illegal moves, because checking for that as part of the training is easy and cheap.

We have models that accurate classify things, e.g. whether or not an email is spam. There isn’t a fundamental limitation into building something like a truth classifier into a generative model so that it optimized for outputting “true” statements. The hardest part is probably identifying what is truth and what is falsehood. That’s a fundamental problem with humanity, not neural networks.

Well, we could quibble about what "fundamental" means but my point is that the way they train large language models doesn't work for this. Something different needs to happen.

Re: Why Meta’s latest large language model survived only three days online

#100

Post by remram »

jleyank wrote: post #3

It’s algorithmically/randomly generating text without understanding. What it the proper way of using it? Fake papers? Political bs? Bad Hemingway (or Shakespeare or Chaucer or…). It’s noise that looks like sentences.

Putting the peer review system to the test?

(I'm not suggesting we should do that)

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