Do you know the difference between ChatGPT and GPT? Are you being misleading on purpose?

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They use the open LAION 50B dataset, everybody knows what's in there.

Still, some preprocessing and deduplication would have been a good idea just for output quality.

Is the amount of context ChatGPT can process really enough for a typical research paper?

I'm in the same boat. Years in this ML business full-time. When I get out of work, I don't want to touch anything tech related, not even with a stick.

Do a two-step. Summarize each paper so the summaries all fit into the context window, then have it compare and contrast.

Yes it is confusing and I don’t think openAI is incentivized to clear up the confusion 😄

No only for gpt3 models such as davinci

Whisper is a voice to text model

Cite one more paper to get 0.15% more chance of being accepted!

The problem I found with chatgpt and other AI is the word limit. I believe it is 4000 words max. and that includes the summary as well.

If anyone knows a fix, please let me know. In the meantime, I use an AI-tool called scholarcy, but it lacks data to be fed with. I study a subject that is *very* reading-heavy, so I can't simply rely on the abstract, and 100 pages per week/course is mostly too much to handle, while working part-time.

Case in point:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3530294/

The title and the abstract are almost disjointed. I come across papers like regularly like maybe 15% of the time?

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Why not int4? Why not pruning? Why not various model compression tricks? int4 halves latency. At minimum they would do mixed int4/int8.

https://arxiv.org/abs/2206.01861

Why not distillation?

https://transformer.huggingface.co/model/distil-gpt2

NVidia using FasterTransformer and Triton inference server has a 32x speed up over baseline GPT-J,

https://developer.nvidia.com/blog/deploying-gpt-j-and-t5-with-fastertransformer-and-triton-inference-server/

I think their assumptions are at least an order of magnitude pessimistic.

As someone else notes, the vast majority of queries can be cached. Also there would likely be a Mixture of experts. No need for the heavy duty model when a trivial model can answer the question.

Yea that certainly seems useful but it also sounds like a mix of search engine and chatgpt. MSs updates to bing might be able to do that?

Probably depends on field? I’ve not typically encountered this and most other researchers are going to be looking at dozens of papers at least so they really don’t want to actually have to dig into a paper to find the meat

Or, click here to auto-cite this paper to learn more about number 14!

Imagine that!

It’s worth noting that both MS and Google own the data centers and hardware so it is likely cheaper for them to run. But still expensive.

> abstract is meant is often a bit clickbaity.

Had a vision of a nightmare future where papers are written in click bait fashion.

Top Ten Shocking Properties of Positive Solutions of Higher Order Differential Equations and Their Astounding Applications in Oscillation Theory. You won't believe number 7!

A lot of people just write “using ChatGPT” in their app headlines when in fact they are actually using the GPT3 API. I will generously interpret this as being due to this genuine confusion :)

Climax my ass, I'm trying to learn, not to cum

Nobody likes having the climax spoiled during the first few pages of a story!

On the training part, it is probably legal, though you need to be careful about something like GDPR. E.g. for facial recognition, there are extra rules.

The "sharing model and/or its prediction" is the gray area.

Edit:t ypo