31–40 of 105 posts
Man if only the authors of such papers would write small summaries about the content and put them in the papers themselves or something
This is a conflict of incentives. Whereas ArxivGPT has no reason not to tell the problems first.
Ive used ChatGPT to make short factual videos for YouTube and honestly it's a bit worrying with supposed 'facts' I would not suggest anyone to use ChatGPT outputs for actual knowledge at this point.
aws cloudfront update-distribution --id --distribution-config --no-reset-origin-access-identity
Took me a while to figure out that the parameter --no-reset-origin-access-identity was not only not working. But it did never exist on any version of the cli tool.
Ive used ChatGPT to make short factual videos for YouTube and honestly it's a bit worrying with supposed 'facts' I would not suggest anyone to use ChatGPT outputs for actual knowledge at this point.
I asked it how to do something with an AWS cli tool. ChatGPT invented a new parameter that looked like a programmer would come up with it and it would do exactly what I was looking for (I assumed many people had my problem before). aws cloudfront update-distribution --id --distribution-config --no-reset-origin-access-identity Took me a while to figure out that the parameter --no-reset-origin-access-identity was not o…
Man if only the authors of such papers would write small summaries about the content and put them in the papers themselves or something
If only the abstracts they write were more often than not accurate and not attention-grabbing problem-ignoring pieces of crap that do not reflect the content of the actual paper, hoping investors/newspapers won't actually read the paper. This is a conflict of incentives. Whereas ArxivGPT has no reason not to tell the problems first.
Earlier quoted context omitted.
I can skim several full pages in the time it takes just to send off the abstract and get the response back...
then batch summarise your paper feed every morning
If I can't, I'm gonna have to skim the paper anyway. But even that could be pretty quick.
Quickly reading something to gauge relevance is something I can confidently say I do much faster than GPT can.
And I don't have a paper feed. I look up papers relevant to what I'm working on at the time.
The example here is a bit worrying for the peer review process. I am not looking forward to my "peers" reviewing my paper by putting it through LLMs and blindly copy pasting the output. I can already imagine emailing the Area Chair and saying "While reviewer 2 is detailed, the questions show a severe lack of basic understanding. We believe the contents are AI generated." Then again, perhaps LLMs could simply be incor…
I like the way you flipped it around. I'm a big believer of using all these new AI services/programs as tools to enhance my workflows, not replacing them.
The example here is a bit worrying for the peer review process. I am not looking forward to my "peers" reviewing my paper by putting it through LLMs and blindly copy pasting the output. I can already imagine emailing the Area Chair and saying "While reviewer 2 is detailed, the questions show a severe lack of basic understanding. We believe the contents are AI generated." Then again, perhaps LLMs could simply be incor…
Don't trust the output to reflect what's in the paper.
Earlier quoted context omitted.
If only the abstracts they write were more often than not accurate and not attention-grabbing problem-ignoring pieces of crap that do not reflect the content of the actual paper, hoping investors/newspapers won't actually read the paper. This is a conflict of incentives. Whereas ArxivGPT has no reason not to tell the problems first.
You're right that abstracts often have a positive bias that can sometimes border on dishonesty, but for most papers it's not a matter of investors or newspapers, but rather of getting the paper accepted to the conference or journal, especially if it's a highly selective one. It happens even in fields where there is no direct industry interest.