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This seems like a great opportunity for some good old fashioned adversarial training. Post train one LLM to please another LLM, that rates the quality of the f

This seems like a great opportunity for some good old fashioned adversarial training. Post train one LLM to please another LLM, that rates the quality of the first model's responses and calls it on any bullshit! (And vice versa.)

IIAOPSW 4y ago
Stable diffusion (of bullshit).
not2b 4y ago
I expect that this will be tried, and I worry about some negative consequences if it works well. It could be a way of generating very effective propaganda, that defeats the efforts of the opposing LLM to call bullshit. On the other hand, diffusion models seem to have replaced GANs for image synthesis, so perhaps there's something I'm missing, or perhaps there's a way to combine both techniques.
- Nevermark 4y ago
  I am not sure calling bullshit on images works the same way, given we generally want to invite creativity to image generation. Although an adversarial extra-finger detector seems in order! Extra fingers are not creativity. They are taboo!! But for text, there is a lot of structure around bullshitting, and fortunately, the internet is full of examples of people calling bullshit. As long as the bullshit adversary has to give a strong critique to back up any bullshit call, it should work. And bullshit judgments can be de-bullshitted too. This would be the same as what our social circles, work colleagues, families, etc. do for us, so a circular firing squad of bullshit judges, who also give gold stars for quality responses, seems like a natural solution for models too.
  - not2b 4y ago
    The first really good image generators for faces (see for example https://this-person-does-not-exist.com https://this-person-does-not-exist.com ) used GANs, which were trained by having one model generate faces and another model saying bullshit, that isn't a face. I'm not worried about using approaches like this to converge on truth (model A generates an answer to a question, model B attempts to refute model A). Perhaps we could train model A to generate lies that model B can't detect.

IIAOPSW 4y ago

Stable diffusion (of bullshit).

not2b 4y ago

I expect that this will be tried, and I worry about some negative consequences if it works well. It could be a way of generating very effective propaganda, that defeats the efforts of the opposing LLM to call bullshit. On the other hand, diffusion models seem to have replaced GANs for image synthesis, so perhaps there's something I'm missing, or perhaps there's a way to combine both techniques.

Nevermark 4y ago
I am not sure calling bullshit on images works the same way, given we generally want to invite creativity to image generation. Although an adversarial extra-finger detector seems in order! Extra fingers are not creativity. They are taboo!! But for text, there is a lot of structure around bullshitting, and fortunately, the internet is full of examples of people calling bullshit. As long as the bullshit adversary has to give a strong critique to back up any bullshit call, it should work. And bullshit judgments can be de-bullshitted too. This would be the same as what our social circles, work colleagues, families, etc. do for us, so a circular firing squad of bullshit judges, who also give gold stars for quality responses, seems like a natural solution for models too.
- not2b 4y ago
  The first really good image generators for faces (see for example https://this-person-does-not-exist.com https://this-person-does-not-exist.com ) used GANs, which were trained by having one model generate faces and another model saying bullshit, that isn't a face. I'm not worried about using approaches like this to converge on truth (model A generates an answer to a question, model B attempts to refute model A). Perhaps we could train model A to generate lies that model B can't detect.