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Comment by magicalhippo | Hacker News Reader

While it certainly seems cool, as with most other AI tools I'm struggling to see how I'd use it. That is, I can't think of anything I'd want to ask.

I assume I'm just getting old and have a limited imagination when it comes to these new AI things.

Anyone got any good examples on how to effectively use this?

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Presumably you're reading the paper for a reason. Take why you're reading the paper and form it into a question.

I struggled with the same problem. What's interesting with LLMs, is that they will augment the data in the paper with their general knowledge. So you can talk to them like you would with a colleague or mentor (but without the shame of asking dumb questions).

But are there currently any good local models that can provide good conversation/support of a recent paper? I feel the level of hallucinations will be huge. Even with gpt4 with pdf2text, it tends to hallucinate a lot. And Gpt4 is the best LLM model so far.

"What's the gist of this thing?"

"I'm building xyz app, how could this be applicable?"

"I want to know if x or y, what does this paper say, or does it even apply?"

"Implement the pseudocode algorithm in python"

"Can you help me understand section X, the wording is tricky"

"Doesn't this suffer from {flaw}?"

"How does the paper address x?"

No, an AI will not give 100% perfect answers all of the time. You know who else? Humans. You already have mechanisms to deal with unreliability, so please save yourself time and use AI to be more efficient.

The questions would come from reading a paper in particular. Have a question? Ask away. That's how I'd use it personally.

What I'm trying to say is I'm not used to think that way. I can't think of a question to ask.

You never have any doubts or uncertainties while reading a paper? Was that always a thing for you or did that grow with experience?

Of course, but then I just go back and reread the relevan section(s).

The things I might be able to phrase into query is not something I trust the AI to be able to explain, like what are the downsides of the proposed algorithm for example (unless explicitly mentioned, which is seldom).

I guess I'll have to try to keep this project in mind next time I read an arXiv article, and give it a spin.

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