ArxivGPT: Chrome extension that summarizes arxived research papers using ChatGPT

I tried asking ChatGPT the knuckle cracking question that GPT-3 got wrong (what happens if you crack your knuckles a lot).

The response was: The sound of cracking knuckles is actually a release of gas in the joint, but there is little scientific evidence to support the idea that it leads to arthritis or other joint problems. In fact, several studies have shown that there is no significant difference in the development of arthritis or other joint conditions between people who crack their knuckles and those who do not.

I wonder if OpenAI used that paper to fine tune ChatGPT

A lot of people cite these numbers as cynics or to dissuade others' optimism, but if you actually work in the field and know what it was like just 5 years ago, these numbers should be extremely worrying to everyone who's afraid of automation. Even that linked paper from 2021 is already outdated. We don't need another revolution, we just need maybe a dozen small to medium insights. The steps from GPT3 to 3.5 alone were pretty straightforward and yet they already created a small revolution in LLM usefulness. Model scale used to slow down the pace of research for a moment, but with so many big companies jumping on the train now, you can expect research to accelerate again.

If you can measure it you can improve it

This paper is a little bit of a red herring, as Yannic, an NLP PhD, covered well here: https://www.youtube.com/watch?v=aX8phGhG8VQ They filtered questions that GPT-3 - the model they tested - got right before constructing the dataset. They asked questions about common misconceptions, conspiracy theories and things humans have believed until recently. The dataset should be called expert consensus vs public entertainment belief i.e. they consider the ground truth as a humourless experts answer, while the reality is in a random sitcom, you -would- expect the meaning derived by the these models (the meaning more common in the training data... such as what happens when a mirror breaks).

It's also clear that RHLF models can be given instructions to reduce this issue. And in many production LLM models, something called few shot is used - in context learning - where you provide several examples and then ask for a new case. The accuracy is again improved this way, because the model "deduces" that you are being serious and humourless, when asking about mirrors breaking, and are not asking in the context of a story.

It's also one of the only datasets that fails to increase with scale (There was a big, cash-paying high incentive challenge to find other datasets, and it pretty much didn't find any that can't be worked around or are not experienced by RLHF models like ChatGPT). So it doesn't represent a wider trend of truthfulness above human accuracy being impossible (dated/data drift on the other hand, clearly remains an issue).

At least in my experience, the aws docs are difficult to understand at best, and horrifically outdated and contradictory at worst.

ChatGPT was my first choice just because I want to know if it is any good at helping me with my work.

But for this problem (CF-Distribution lost OAC settings when updating the root file), all the google fu in the world did not help me. It turned out that I had to update my aws-cli and my problem went away. Apparently no one else on the internet had that problem, so only my gut could help me figure it out.

Conferences are often done just because it's a requirement to get grants/continue to be sponsored by an university. Investors are not just VC/for-profit industry, academia system also counts.

You're assuming, not projecting.

I think we're in uncharted waters to some extent and should have some restraint about predictions.

I don't know man, maybe I'm wrong. I've been using HN for over a decade now, I've found constructive criticism to be a huge part of the positive culture here. It can of course be selection bias as I try to read more of that stuff and collapse a thread as soon as it gets into less constructive direction.

But on the other hand, this is what the guidelines say

> Please don't post shallow dismissals, especially of other people's work. A good critical comment teaches us something.

I don't think these type of comments teach us anything. But these are guidelines and not rules for a reason.

This is such a false equivalence. We are talking here about adults who want to build something cool. If we are going to define to scope of everything as "useful for everyone and 100% safe from the beginning" I think we'll get nothing done. If you think the risk here is equivalent, I really don't know what to say to you.

I think you hit the nail on the head, it does frustrate me. Not only because it's repeated often, but also because it's applied equally everywhere. Look at this project for example. It's an extension for arXiv, a pre print repository for cutting edge research, do we really need to keep the entirety of humanity in mind while making this? Because that's what the original comment was saying. The way I see it, arXiv is mostly looked at by experts who would know when something is completely off, and will probably look into things further than reading the GPT summary. If I was still actively doing research and hitting the site every day, this would have immensely streamlined my flow (maybe with a few tweaks to the prompt to emphasize certain sections which I'm usually more interested in).

But that's not how any of this is discussed. That's where my scope comment comes from, the scope of every project cannot be "for everyone and 100% safe from the beginning". In this way there is no encouragement to discuss/make better things, just discouragement. I, personally, hate this.

Abstracts can also be close to a full page long. While this doesn't have to be bad, it's usually more information you're looking for (especially if you were only going to read the abstract anyway).

Length isn't always the indicator. Abstracts can be information dense, and sometimes more words is simpler to understand, or sometimes it just has jargon. For example, this paper on GPT-3:

"Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine- tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning. with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."

The summary:

- If you train a computer on a lot of words, it can do things a lot better

- In this case, the computer has learned how to translate languages, answer questions, and unscramble words

- It still has trouble with some things

- This is very interesting because it shows computers can do things which are very hard.

So here, it drops a lot of information (cloze tasks), and it adds some (that last point). But now I know what the paper is about in 10 seconds.

I go back and see that oh, "a lot of words" really means 10x the previous. I'm now hooked on what problems it has trouble with, and what problems it solves for humans.

I didn't know a thing about LLMs when I first read this. If I tried to read it top to bottom, I'd get stuck on "task-agnostic, few-shot performance" then "state-of-the-art fine- tuning approaches" then "an autoregressive language model". They're big words, but turns out they're not the interesting parts, and understanding what the paper is excited about helped me to understand the basics.

Well yes in a way, the extension settings include the prompt they're using, so you can formulate your own prompt along the same lines.

I think the extension is only useful for someone who spends a lot of time doing research on arxiv.org, (and if the quality of the summary is good enough, the jury's still out on that one)