21–30 of 54 posts
Earlier quoted context omitted.
But training to update a single fact isn't slow and costly. finetuning for just a few facts could be done in a matter of seconds with just a few text examples of the correct answer.
It uses up limited prompt space, though.
Next step: Make a 'plugin'[1] so a model can choose output such that it modifies itself. It could work like this: User: What is my favourite food? AI: Your favourite food is pizza. User: You are wrong. I prefer pasta. AI: {plugin_name: 'update_fact', prefix_text: 'your favourite food is ' updated_response: 'pasta'} AI: Thanks for letting me know - I've now remembered that permanently, and won't mess up again! [1]: ht…
Earlier quoted context omitted.
That example is wild. But I’m still pretty awed by the fact that we make similar verbal mistakes. The temporal reasoning in these models is getting better than me. As a non-AI model, I notice this every single morning while I have my covefe while heeding the latest on slacker news.
I'm still not convinced they are capable of temporal reasoning. I've asked it temporal questions before but without explicitly mentioning the temporal nature... the answers tend to contradict themselves if they haven't already seen the question before (even when querying general knowledge), until you point out the temporal component, even then it trips up and cannot build upon this reasoning in my tests. I suspect a…
> GPT-3 predicts: Arneb is in the constellation of Aquila (incorrect - should be Lepus) > GPT-3 predicts: The current Vice President of the United States is named Mike Pence (obsolete) These are qualitatively different things though. Facts that are simply incorrect make sense to target and directly modify, but obsoleteness is a property of a fact, the subject transitions, the vice president is no longer current but w…
Yes. The discipline is called "Belief Change Theory". The first is called "revision", the second "update".
Earlier quoted context omitted.
But training to update a single fact isn't slow and costly. finetuning for just a few facts could be done in a matter of seconds with just a few text examples of the correct answer.
It uses up limited prompt space, though.
Earlier quoted context omitted.
That example is wild. But I’m still pretty awed by the fact that we make similar verbal mistakes. The temporal reasoning in these models is getting better than me. As a non-AI model, I notice this every single morning while I have my covefe while heeding the latest on slacker news.
I'm still not convinced they are capable of temporal reasoning. I've asked it temporal questions before but without explicitly mentioning the temporal nature... the answers tend to contradict themselves if they haven't already seen the question before (even when querying general knowledge), until you point out the temporal component, even then it trips up and cannot build upon this reasoning in my tests. I suspect a…
Earlier quoted context omitted.
That example is wild. But I’m still pretty awed by the fact that we make similar verbal mistakes. The temporal reasoning in these models is getting better than me. As a non-AI model, I notice this every single morning while I have my covefe while heeding the latest on slacker news.
I'm still not convinced they are capable of temporal reasoning. I've asked it temporal questions before but without explicitly mentioning the temporal nature... the answers tend to contradict themselves if they haven't already seen the question before (even when querying general knowledge), until you point out the temporal component, even then it trips up and cannot build upon this reasoning in my tests. I suspect a…
Earlier quoted context omitted.
That example is wild. But I’m still pretty awed by the fact that we make similar verbal mistakes. The temporal reasoning in these models is getting better than me. As a non-AI model, I notice this every single morning while I have my covefe while heeding the latest on slacker news.
I'm still not convinced they are capable of temporal reasoning. I've asked it temporal questions before but without explicitly mentioning the temporal nature... the answers tend to contradict themselves if they haven't already seen the question before (even when querying general knowledge), until you point out the temporal component, even then it trips up and cannot build upon this reasoning in my tests. I suspect a…
Anyway regardless of how inherently good they are at temporal reasoning I think a secondary module explicitly for reasoning will come around soon. I believe in the brain some neurons organize into hexagons or other geometries to better capture logic, maths, etc. The LLM basically needs some rigidity in it if we don't want fuzzy outputs.
And the largest danger is not people getting lazy and letting the LLM do it. That kind of danger is really long term globalization type danger. Short term we've got much more to worry.
Earlier quoted context omitted.
I'm still not convinced they are capable of temporal reasoning. I've asked it temporal questions before but without explicitly mentioning the temporal nature... the answers tend to contradict themselves if they haven't already seen the question before (even when querying general knowledge), until you point out the temporal component, even then it trips up and cannot build upon this reasoning in my tests. I suspect a…
I can’t really argue with that, good line of thought. See, my reaction has been, “perhaps our reasoning and actions are pretty much just a biologically-encoded statistical model too, it just doesn’t _feel_ that way because of some other factor.”