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Can LLMs Perform Deep Technical Comprehension of Computer Architecture Papers

arxiv.org

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Re: Can LLMs Perform Deep Technical Comprehension of Computer Architecture Papers

#12
post #6

I recently just referenced the selective applicative functors paper and let it write me an implementation in scala. There is one already available in github, so I can't judge if it really just read the paper and implemented it, but the result was so minimal quick and amazing.

Well if you can't judge, doesn't it make your comment completely useless?

No, I don't think OP's comment is useless. It's not just a blanket statement, "Wow, LLM's quick", but also critically reflective, like a brief limitations section. This opens the discussion, by indirectly posing questions like, "Is it really this fast or "cheating"? How could we measure this experimentally? Etc.

Re: Can LLMs Perform Deep Technical Comprehension of Computer Architecture Papers

#13
post #4

Actually deep reasoning, can't reason without comprehension. This will make sense for technical uses as it's intended. I can see this being very useful for code error mitigation and fixes.

Isn't the whole point of attention, some context comprehension in the stochastic parrot machine.

Yes, however I do suspect that at some point comprehension trumps context. Meaning it will be evedent giving the ability to increase comprehension and retaining whatever context you have goes a long way. I dont have the ability to play such large models. Within the means of my hardware I have already playd around with LLM enough to know where balances will start to cause confliction. Priotizing effeciency within the accuracy. This can mean compromising some other aspect of the AI, or this can be viewed as a positive interoperability.

Re: Can LLMs Perform Deep Technical Comprehension of Computer Architecture Papers

#14

The abstract is AI generated and pretty poorly written at that. A paper about grading AI output doesn’t even grade their own abstract.

Can’t believe the abstract has an em dash, a “not X but Y” and a “rule of three” in the first sentence. This is ridiculous.

Re: Can LLMs Perform Deep Technical Comprehension of Computer Architecture Papers

#16

The abstract is AI generated and pretty poorly written at that. A paper about grading AI output doesn’t even grade their own abstract.

Can’t believe the abstract has an em dash, a “not X but Y” and a “rule of three” in the first sentence . This is ridiculous.

The reference implementation of slop :)

Speaking of which, does anyone know a resource that lists these "tells"? I used to notice them all the time, but now that they've permeated 2/3rds of what I see and hear, I'm starting to go a bit numb.

Re: Can LLMs Perform Deep Technical Comprehension of Computer Architecture Papers

#18
post #16

Earlier quoted context omitted.

Can’t believe the abstract has an em dash, a “not X but Y” and a “rule of three” in the first sentence . This is ridiculous.

The reference implementation of slop :) Speaking of which, does anyone know a resource that lists these "tells"? I used to notice them all the time, but now that they've permeated 2/3rds of what I see and hear, I'm starting to go a bit numb.

Possibly https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing

Re: Can LLMs Perform Deep Technical Comprehension of Computer Architecture Papers

#19
post #16

Earlier quoted context omitted.

Can’t believe the abstract has an em dash, a “not X but Y” and a “rule of three” in the first sentence . This is ridiculous.

The reference implementation of slop :) Speaking of which, does anyone know a resource that lists these "tells"? I used to notice them all the time, but now that they've permeated 2/3rds of what I see and hear, I'm starting to go a bit numb.

Here https://en.wikipedia.org/wiki/Literary_device

Re: Can LLMs Perform Deep Technical Comprehension of Computer Architecture Papers

#20
post #4

Earlier quoted context omitted.

Isn't the whole point of attention, some context comprehension in the stochastic parrot machine.

Yes, however I do suspect that at some point comprehension trumps context. Meaning it will be evedent giving the ability to increase comprehension and retaining whatever context you have goes a long way. I dont have the ability to play such large models. Within the means of my hardware I have already playd around with LLM enough to know where balances will start to cause confliction. Priotizing effeciency within the…

> comprehension trumps context

Consider context as hyper-dimensional coordinate vectors gesturing at the starting concept cluster of a synthesis chain or thread to unspool.

If the model's comprehensive training activated by your context locates the right thread to pull, this could be considered comprehension? That it "got" it?

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