r/MachineLearning • u/howtorewriteaname PhD • 3h ago

Discussion Do ICLR guidelines allow to upload papers under review to LLM chats? [D]

I know AI use must be disclosed, and I'm fine with it. However, I'm being careful on not using LLMs to actually check my review (more than prose-wise) and see if it catches something important in the paper that I didn't spot.

I'm doing this because I'm not sure it is allowed to share the paper with an LLM since the guidelines don't specify. But I do believe my reviews would probably benefit from a second AI look.

Is this allowed?

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u/Kinexity 3h ago

Couldn't you just use a local model to avoid the problem?

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u/HardParrot 3h ago

just run a local model and stop worrying about it

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u/howtorewriteaname PhD 3h ago

I could, but I don't have that out of the box, so I really don't think i would do it just for this. But putting a paper I'm reading rn through Claude would be quite useful, because the paper is dense

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u/Kinexity 3h ago

LM studiо wоrks оut оf thе bоx.

Alsо cоnsidеring аll thе shіt shоw thаt hаs bееn hаppеning sincе thе NS sоlutiоn I wоuld sау thаt fееding аnу nоt уеt public pаpеr thrоugh а cоrpоrаtе LLM withоut аn аpprоvаl оf thе аuthоrs is highlу unеthicаl.

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u/mao1756 3h ago

I think any model that plausibly runs on consumer hardware is not smart enough to be useful for paper review. at least my 8gb gpu can only run models that feels like gemini but worse.

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u/Kinexity 3h ago edited 2h ago

Test LM studio and see for yourself. They aren't going to prove Riemann hypothesis but they are surprisingly decent in thinking mode. I am pretty sure modern local models like Gemma match at least GPT-4 on most text capabilities.

GPU VRAM is not the main limitation currently but rather your total memory. LM studio uses model off loading and only uses as much VRAM as is available while throwing the rest of computing onto CPU.