Thanks for the post! I think the main problem is that the abstract does not give enough feel for the core content of the paper, and so people are mostly not trying to dive into paper (they can’t evaluate from the abstract whether it is promising enough to be worth an effort).
I uploaded the paper PDF into GPT-5 Thinking and I asked
Hi, I am trying to get a high-level summary of the text I just uploaded. I have read its abstract, but I don’t know what the TTQ stands for, or what are the main ideas used to formulate the Outer Alignment Precondition and the TTQ.
and the model produced a couple of pages of a detailed summary:
I wonder if this can be formulated in a more readable fashion to be included into the abstract, so that the readers of the abstract would have a better impression of what’s inside the paper.
Hi mishka, thanks for commenting. TBH LLM-based chatbots don’t really understand either their input prompts or their output continuations, so any LLM-based summary is not going to be particularly reliable. Subsequent to your comment, I have added a TL;DR section, borrowed from the paper’s introduction—I hope this helps at least a little. Other than that, I’m afraid there’s no real substitute for actually reading the paper in full—I hope you choose to do so!
Thanks for the post! I think the main problem is that the abstract does not give enough feel for the core content of the paper, and so people are mostly not trying to dive into paper (they can’t evaluate from the abstract whether it is promising enough to be worth an effort).
I uploaded the paper PDF into GPT-5 Thinking and I asked
and the model produced a couple of pages of a detailed summary:
https://chatgpt.com/share/68a5faef-c050-8010-8392-20772cd6a370
I wonder if this can be formulated in a more readable fashion to be included into the abstract, so that the readers of the abstract would have a better impression of what’s inside the paper.
Hi mishka, thanks for commenting. TBH LLM-based chatbots don’t really understand either their input prompts or their output continuations, so any LLM-based summary is not going to be particularly reliable. Subsequent to your comment, I have added a TL;DR section, borrowed from the paper’s introduction—I hope this helps at least a little. Other than that, I’m afraid there’s no real substitute for actually reading the paper in full—I hope you choose to do so!
Thanks, that helps!