this post was submitted on 14 Jul 2023
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Wow, very nice! First of all, I will preface by admitting that I have not worked with LLMs to the degree of making a toy implementation. Your explanation of the sampling techniques is insightful but doesn't clear up my confusion. Why does sampling imply the absence of higher level structure in the model?
For example, even though poker is highly influenced by chance, I can still have a plan that will increase my likelihood of winning. I don't know what card will be drawn next but I can prepare strategies for each possible card. I can have preferences for which cards I want to be drawn next.
You know what, I don't have a good answer to you here. I did a few small experiments on ChatGPT and it seems like it has some knowledge of if it will be able to complete it or not. This was with a pretty well known question though.
I tried to recreate an earlier experiment where I asked it to write about a friend of mine, which was in the news some time ago and have apparently a few entries in it's training data, but very little. ChatGPT would then consistently hallucinate facts about the person, including date of birth and sometimes date of death. In that case it knew the pattern of writing about a person including date of birth, and sometimes date of death, but it didn't know it didn't have that info and just filled in plausible looking data there. Now it insists on not knowing who that person is at all and refuses to write anything about him.
Anyway, you've given me some things to think about, thanks.