tbf, you cite the paper that described and discussed said solution in the more appropriate form

In some fields, sure, cite the 4chan source, ideally with an archived link.

Pure math tends to be much more conservative in citations than other fields though, and even when writing a paper about a longstanding math problem you wouldn't necessarily bother to include existing solutions. You reference the things you actually used, and even then you assume some common background knowledge for your audience and don't reference every little undergrad topology theorem or whatever. The point is to be honest with the reader about what was helpful for this work in particular, both to properly attribute things you actually used and to make any searches based on your work more targeted and fruitful.

And people who care more for gatekeeping will stick to academic echo chambers. The list of community driven medical discoveries encountering entrenched professional opposition is quite long.

Both models are fallible, which is why discernment is so important.

You can do that with reviewed papers too :)

Often reviewing is executed double blind for exactly this reason. This can be difficult in small fields where you can more-or-less guess who's working on what, but the intent is definitely there.

I've reviewed computational linguistics papers in the past (I'm retired now, and the field is changing out from under me, so I don't do it any more). But all the reviews I did were double blind.

It's what we train LLMs on to make them learn language, a thing that all healthy adult human beings are experts on using. It's definitely not what we train LLMs on if we want them to do science.

Which are known to be unreliable beyond basic things that most people that have some relevant experience get right anyway.

There's a paper Textbooks are all you need - https://arxiv.org/abs/2306.11644

> We introduce phi-1, a new large language model for code, with significantly smaller size than competing models: phi-1 is a Transformer-based model with 1.3B parameters, trained for 4 days on 8 A100s, using a selection of ``textbook quality" data from the web (6B tokens) and synthetically generated textbooks and exercises with GPT-3.5 (1B tokens). Despite this small scale, phi-1 attains pass@1 accuracy 50.6% on HumanEval and 55.5% on MBPP. It also displays surprising emergent properties compared to phi-1-base, our model before our finetuning stage on a dataset of coding exercises, and phi-1-small, a smaller model with 350M parameters trained with the same pipeline as phi-1 that still achieves 45% on HumanEval

We train on the internet because, for example, I speak a fairly niche English dialect influenced by Hebrew, Yiddish and Aramaic, and there are no digitised textbooks or dictionaries that cover this language. I assume the base weights of models are still using high quality materials.

There's no problem in letting anyone upload. The problem is in claiming that we should give the same amount of attention to the work of anyone, that "only what is said matters". Just like we don't run random code off github, we have no reason to read random papers on arxiv. And, even on github, anyone using a project knows that "who is maintaining this project" is a major decision factor.

You can't comment like this on Hacker News, no matter what you're replying to.

https://news.ycombinator.com/newsguidelines.html