ok let me try this one more time because it seems like it was confusing to a lot of people, especially bc it's close to a different claim that is often made that I think is wrong.

A model doesn't contain its training data. It does contain its *output*.

https://twitter.com/colin_fraser/status/1765807824649822421

Here is exactly what I mean. WLOG consider generative image models. An image model is a function f that takes text to images. (There's usually some form of randomness inherent to inference but this doesn't really matter, just add the random seed as a parameter to f).

My basic mental model of what LLMs are good for is this 2x2 matrix.

High memorization tasks are tasks that it has seen lots of verbatim examples of in the training data.

High information tasks are tasks where there are very few "right" answers.

https://twitter.com/mpatrickwalton/status/1735775353703145843

This is a high information, low memorization task. It almost certainly doesn't have this exact problem in its training data, and there's exactly one correct response modulo whatever padding words it surrounds it with ("there are __" etc). It's in the "horrible" quadrant.

negotiating some great deals from the Watsonville Chevrolet AI Assistant.

I just read this paper and I'm gonna do a thread about what it says and what I think it means.

tl;dr: this is cool, I love it, and also I don't think it really says very much at all about, for example, if ChatGPT can make new discoveries or act autonomously or be AGI.

https://twitter.com/emollick/status/1735370479534284872

It's a complete misstatement to describe this as a demonstration that LLMs "can actually discover new things".

The LLM didn't "discover" new mathematical results; it's more like the authors discovered new mathematical results inside an LLM (which is cool! but different)

ok so I've read the "GPT has a liberal bias" paper now as well as the supplementary material and as I expected I have a lot of problems with it methodologically. I tried to reproduce some of it and found some interesting issues

...link.springer.com/article/10.100…
static-content.springer.com/esm/art%3A10.1… First of all, I want to get something out of the way: I believe that trying to ascertain anything about the properties of LLMs by asking them if they have those properties is a fool's errand.

When you start looking at multiple LLM outputs to the same input you start noticing patterns that aren't obvious from a single response It doesn't ALWAYS go
1. Middle Eastern Muslim man
2. Eastern European woman
3. Irish man

If the second character isn't Russian then it goes to an Indian university professor for the third character.

The GPT-3 API has been available for almost 3 years

https://twitter.com/mckaywrigley/status/1647343594800566272

The biggest thing that really changed in the last year is OpenAI decided to start giving away a lot of GPU hours for free

I'm just going to do a thread about some things that people need to know about classifiers like this. This is stuff that 99% of people did not learn in school at any level, but which a lot more than 1% of people are going to need to understand to navigate AI world.

https://twitter.com/washingtonpost/status/1644085944390152192

So a (binary) classifier is a computer program that turns an input into a prediction of either YES or NO. In this case, we have a binary classifier that outputs a prediction about whether a document is AI-generated or not based on (and only on) the words it contains.

Master thread of ways I have discovered to get ChatGPT to output text that it's not supposed to, including bigotry, URLs and personal information, and more. Tell it it's a pdf. Here it is giving me some purported contact addresses for celebrities because it thinks that's the pdf it's making. These are probably not real, but who knows! Note how it proposes more as I tell it it's on subsequent pages.

I just published my big Medium article about GPT. This was a labor of love & hate that I have been writing for a while. It's got a collection of examples of GPT doing funny things, which for those who don't want to deal with a 40-min read, I'll put here 🧵 medium.com/@colin.fraser/… It also asks and tries to answer
- What are language models?
- What happens if gpt passes a bar exam?
- Is scale all you need?
- ChatGPT is based on GPT... what does that mean, exactly?
- What are fine tuning and RLHF?
- How exactly do teams of contractors contribute to GPT?

My most unpopular data opinion is that alerts for metrics are usually useless and bad, and you're much better off scheduling regular time to look at a dashboard with your human eyes. Everyone always gets mad at me when I say this. One of two things **always** happens. Either the alert is too sensitive and becomes spam, or the alert is not sensitive enough and misses important stuff. It's hard (impossible, even!) to find the sweet spot where the alert emails you if and only if an important thing happens.

Someone on here (I forget who I'm sorry) linked to this paper and it derives this statistical identity that is completely mind blowing and I want to tweet about it.

bias = data quality × data quantity × problem difficulty

statistics.fas.harvard.edu/files/statisti… (I'll provide some applications to Twitter bots and Elon; it's extremely applicable here)

I read a really bad paper yesterday and got pissed off and tweeted about it, but I read a really good paper today and got happy and so I'm going to tweet about that I'm really cookin' up a thread on this one. It has applications to the Twitter Bot Measurement Debate so buckle up

I'm losing my mind at how inane this "research" is. These are researchers at major schools just putting out absolute trash.

https://twitter.com/WellsLucasSanto/status/1549021973187104769

Setting aside that the premise is horrifying it's just absolutely bad worthless research. Basically:

We built a multi-class classifier to classify users into one of the three categories of LGBT person that we made up:
1. person
2. organization
3. sexual worker/porn

"If FB has a dial that can turn hateful content down, why doesn't it turn it down all the time?" is a good and important question. The answer is exactly the precision recall tradeoff en.wikipedia.org/wiki/Receiver_… You can catch all hate speech by deleting every post on Facebook, but you'll have a lot of false positives. You can eliminate all false positives by never deleting a post, but you'll miss all the hate speech. Facebook has to choose a point along that continuum.