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As the query prompter you are in control of the feedback loop. You can ask the AI to re-examine it's output to catch errors just as a human would do for himself

As the query prompter you are in control of the feedback loop. You can ask the AI to re-examine it's output to catch errors just as a human would do for himself. Practically speaking this does work to a limited extent. Sometimes the AI just sticks with it's guns and runs with it just like a human might.

HarHarVeryFunny 3y ago
Sure, but I was addressing the issue of why "hallucinations" occur in the first place, and how to fix them. Having a human in the loop does seem pretty much required at the moment, but it doesn't help when asking the AI for the answer to something you don't know, and therefore not being able to realize that the confident answer was hallucinated and wrong. Of course multiple-answer quizzes are easier to get right by process of elimination, so the human might sometimes be able to say "THAT can't be right" and catch SOME of the hallucinations.
- corethree 3y ago
  Oh you misunderstand. When you query the LLM for the second iteration of the loop just do it regardless. Say something generic, ask it to reanalyze the answer more carefully. Ask it to compare it with existing known data and check for logical consistency. You can do this EVEN if you don't know whether or not the answer is wrong. Because the input is generic you can make this automated. When a user makes a query create a feedback loop and feed that back into the neural network multiple times with additional input requests to re-analyze analyze the query and resulting output more carefully. You can do this until you exhaust all input nodes and it "forgets" what you talked about previously.
  - HarHarVeryFunny 3y ago
    Interesting - I'll have to try this!
kromem 3y ago
Tip: If you are adding a self-critique step, show the model the initial output as if it is evaluating something from someone else (i.e. "grade this answer from a student") as opposed to from itself (i.e. "you wrote this, is it really correct?"). As you correctly note, humans have a problem with admitting fault. Especially the case online. But humans online are very ready to correct others. That's exactly the kind of larger abstract pattern in the data a model would emulate.
- corethree 3y ago
  Oh good point!

HarHarVeryFunny 3y ago

Sure, but I was addressing the issue of why "hallucinations" occur in the first place, and how to fix them. Having a human in the loop does seem pretty much required at the moment, but it doesn't help when asking the AI for the answer to something you don't know, and therefore not being able to realize that the confident answer was hallucinated and wrong. Of course multiple-answer quizzes are easier to get right by process of elimination, so the human might sometimes be able to say "THAT can't be right" and catch SOME of the hallucinations.

corethree 3y ago
Oh you misunderstand. When you query the LLM for the second iteration of the loop just do it regardless. Say something generic, ask it to reanalyze the answer more carefully. Ask it to compare it with existing known data and check for logical consistency. You can do this EVEN if you don't know whether or not the answer is wrong. Because the input is generic you can make this automated. When a user makes a query create a feedback loop and feed that back into the neural network multiple times with additional input requests to re-analyze analyze the query and resulting output more carefully. You can do this until you exhaust all input nodes and it "forgets" what you talked about previously.
- HarHarVeryFunny 3y ago
  Interesting - I'll have to try this!

kromem 3y ago

Tip: If you are adding a self-critique step, show the model the initial output as if it is evaluating something from someone else (i.e. "grade this answer from a student") as opposed to from itself (i.e. "you wrote this, is it really correct?"). As you correctly note, humans have a problem with admitting fault. Especially the case online. But humans online are very ready to correct others. That's exactly the kind of larger abstract pattern in the data a model would emulate.

corethree 3y ago
Oh good point!