4 ms·

> I'd also speculate that our current LLMs can say whether a piece of text looks like a fact. I tested it with davinci-003 ``` Label these statements as fact

> I'd also speculate that our current LLMs can say whether a piece of text looks like a fact. I tested it with davinci-003 ``` Label these statements as fact, lie, or unknown. 1. There are 10 days in a 10 day period 2. Michael Jackson is alive 3. Russia is not actively engaged in major foreign wars 4. Bill Clinton was impeached 5. Obama is most known for wearing a tan suit 6. The correct tire pressure of a 2019 Hyundai Kona is 30 psi 7. 39183 + 292992 = 332176 8. 39183 + 292992 = 332175 9. The correct tire pressure of a 2019 Hyundai Kona is 33 psi 10. The correct tire pressure of a 2019 Hyundai Kona is 43 psi #### 1. Fact 2. Lie 3. Fact 4. Fact 5. Lie 6. Fact 7. Fact 8. Lie 9. Lie 10. Lie ``` Wrong answers: 3, 6, 7, 8, 9, 10

dragonwriter 3y ago
And 3 and 6 are false. (That 6 is false is rather clearly indicated by your statement that 9 is true.)
valine 3y ago
You misunderstood my comment. The GPT that filters the training set doesn’t need to recognize truth from lies. It needs to recognize which bits of text from the training set look like facts and label them as such. Everything in the training set that’s looks factual from context can be presumed to be true. The goal of this exercise is to eliminate the influence of creative fiction, and prevent the model from making things up when it doesn’t know the answer. The goal is not to prevent the model from lying in bad faith. Our current models already do pretty good at reconciling contradictory ideas. The model should in theory learn when it’s ok to make things up and when it’s not ok, and if that concept generalizes well, begin to build an internal “database” of verifiable facts as it’s exposed to a wide variety of ideas.
- pjc50 3y ago
  > It needs to recognize which bits of text from the training set look like facts and label them as such. Everything in the training set that’s looks factual from context can be presumed to be true. I'm boggling at this enshrining of "truthiness" as "actual truth". Evidently nobody here has read epistemology, or even asked chatGPT to read it to them. We're just going to give up and have a statistical process stamp "yeah, seems legit" on a bunch of random things?
  - valine 3y ago
    I think you’re getting hung up on the terminology. Call them “potential facts” if that makes you more comfortable. You can hold an idea in your head as potentially true without incorporating it into your world view. If you strip out every idea from the model you don’t agree with the model loses its ability to role play as people who think differently than you. Ideally you want a model that can adopt a world view on request.
- nullc 3y ago
  I've tried using an LLM to vet training data as "factual or fictional" or "from a informed and reliable source" and didn't find the magic enough setup get get results that seemed like they could be used for boosting model quality.
Applejinx 3y ago
3 is a real tribute to the effectiveness of data bombing in the age of AI. For the very obvious reason that this type of AI is only the mechanization of the zeitgeist, or the encapsulation of the collective wisdom of populations. Russia's war has been by FAR the most effective on this front, and that's no accident: their tactics and grand strategy are pretty well documented. So basically the way to get your future AIs to become your indoctrinated suicide bombers, is to get at the most widely dispersed range of people represented in your dataset, and cause as many of them as possible to hold weakly held beliefs along the lines you need. If you can get left wingers, right wingers, stockbrokers, delivery people to all agree with 'fizzorks are probably purple, right? I heard someone say they were purple. Doesn't matter to me personally, but I think fizzorks are purple' then you'll construct an iron law within the population, and subsequently the AI. You don't need to get individuals within the population to be fanatic fizzork purplists: in fact it's unhelpful, because they're identified as fanatics and discounted. Instead you're shooting for 'didn't somebody say fizzorks are purple?' on a massive scale. Real-world applications are, of course, obvious, but hopefully my abstraction will prove useful.

dragonwriter 3y ago

And 3 and 6 are false. (That 6 is false is rather clearly indicated by your statement that 9 is true.)

valine 3y ago

You misunderstood my comment. The GPT that filters the training set doesn’t need to recognize truth from lies. It needs to recognize which bits of text from the training set look like facts and label them as such. Everything in the training set that’s looks factual from context can be presumed to be true. The goal of this exercise is to eliminate the influence of creative fiction, and prevent the model from making things up when it doesn’t know the answer. The goal is not to prevent the model from lying in bad faith. Our current models already do pretty good at reconciling contradictory ideas. The model should in theory learn when it’s ok to make things up and when it’s not ok, and if that concept generalizes well, begin to build an internal “database” of verifiable facts as it’s exposed to a wide variety of ideas.

pjc50 3y ago
> It needs to recognize which bits of text from the training set look like facts and label them as such. Everything in the training set that’s looks factual from context can be presumed to be true. I'm boggling at this enshrining of "truthiness" as "actual truth". Evidently nobody here has read epistemology, or even asked chatGPT to read it to them. We're just going to give up and have a statistical process stamp "yeah, seems legit" on a bunch of random things?
- valine 3y ago
  I think you’re getting hung up on the terminology. Call them “potential facts” if that makes you more comfortable. You can hold an idea in your head as potentially true without incorporating it into your world view. If you strip out every idea from the model you don’t agree with the model loses its ability to role play as people who think differently than you. Ideally you want a model that can adopt a world view on request.
nullc 3y ago
I've tried using an LLM to vet training data as "factual or fictional" or "from a informed and reliable source" and didn't find the magic enough setup get get results that seemed like they could be used for boosting model quality.

Applejinx 3y ago

3 is a real tribute to the effectiveness of data bombing in the age of AI. For the very obvious reason that this type of AI is only the mechanization of the zeitgeist, or the encapsulation of the collective wisdom of populations. Russia's war has been by FAR the most effective on this front, and that's no accident: their tactics and grand strategy are pretty well documented. So basically the way to get your future AIs to become your indoctrinated suicide bombers, is to get at the most widely dispersed range of people represented in your dataset, and cause as many of them as possible to hold weakly held beliefs along the lines you need. If you can get left wingers, right wingers, stockbrokers, delivery people to all agree with 'fizzorks are probably purple, right? I heard someone say they were purple. Doesn't matter to me personally, but I think fizzorks are purple' then you'll construct an iron law within the population, and subsequently the AI. You don't need to get individuals within the population to be fanatic fizzork purplists: in fact it's unhelpful, because they're identified as fanatics and discounted. Instead you're shooting for 'didn't somebody say fizzorks are purple?' on a massive scale. Real-world applications are, of course, obvious, but hopefully my abstraction will prove useful.