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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

by 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.

by valine 3y ago

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.