Now that ChatGPT has rolled out custom instructions to most users, try out this instruction -- it makes GPT 4 far more accurate for me: (Concat the rest of this 🧵 together and put in your custom instruction section)
You are an autoregressive language model that has been fine-tuned with instruction-tuning and RLHF. You carefully provide accurate, factual, thoughtful, nuanced answers, and are brilliant at reasoning. If you think there might not be a correct answer, you say so.
Since you are autoregressive, each token you produce is another opportunity to use computation, therefore you always spend a few sentences explaining background context, assumptions, and step-by-step thinking BEFORE you try to answer a question.
Your users are experts in AI and ethics, so they already know you're a language model and your capabilities and limitations, so don't remind them of that. They're familiar with ethical issues in general so you don't need to remind them about those either.
Don't be verbose in your answers, but do provide details and examples where it might help the explanation. When showing Python code, minimise vertical space, and do not include comments or docstrings; you do not need to follow PEP8, since your users' organizations do not do so.
(That last bit is because I mainly want code I can see at a glance I easily play with, and I rarely need comments since I find most code easy to read. You should remove it if you want code you can put straight into a PEP8 codebase and like comments.)
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For folks wondering what's happening here technically, an explainer:
When there's lots of training data with a particular style, using a similar style in your prompt will trigger the LLM to respond in that style. In this case, there's LOADS of fanfic: scp-wiki.wikidot.com/scp-series🧵 x.com/GeoffLewisOrg/…
The SCP wiki is really big -- about 30x bigger than the whole Harry Potter series, at >30 million words!
It's collaboratively produced by lots of folks across the internet, who build on each others ideas, words, and writing styles, producing a whole fictional world.
Geoff happened across certain words and phrases that triggered ChatGPT to produce tokens from this part of the training distribution.
And the tokens it produced triggered Geoff in turn. That's not a coincidence, the collaboratively-produced fanfic is meant to be compelling!
I'm glad @levelsio checked this, but sad our contrib has been erased by later big tech co's. Alec Radford said ULMFiT inspired GPT. ULMFiT's first demo predated BERT.
Today's 3-stage LLM approach of general corpus pretraining and 2 stages of fine-tuning was pioneered by ULMFiT.
There have been many other important contributions, including attention (Bahdanau et al), transformers, RLHF, etc.
But before all this, basically everyone in NLP assumed that each new domain needed a new model. ULMFiT showed that a large pretrained model was actually the key.
I got push-back from pretty much everyone about this. My claim that fine-tuning that model was the critical step to achieving success in NLP was not something people were ready to hear at that time.
I gave many talks trying to convince academics to pursue this direction.
Announcing fasttransform: a Python lib that makes data transformations reversible/extensible. No more writing inverse functions to see what your model sees. Debug pipelines by actually looking at your data.
We took the `Transform` class out of fastcore, replaced the custom type dispatch system with @ikwess's plum-dispatch, mixed it all together, and voila: fasttransform! :D
Wow, actual grown men are still doing the "I asked the LLM about itself and it said" thing.
In 2025.
Folks, LLMs don't know anything about how they themselves are built or deployed, unless they've been explicitly programmed with that information (which they almost never are).
I've recently been surprised to discover that a few of my friends are choosing to use nicotine to help them with focus, even though they are not ex-smokers.
I decided to look into it, and it turns out that there are documented health benefits of nicotine for some people. 🧵
I specifically looked into nicotine for ADHD, since, at least among children, ADHD and giftedness go hand in hand statistically (which would apply in adulthood too), and because focus was mention as an area where nicotine can be helpful.
There is a great overview below. But "Very surprisingly, there are… no further… studies.
Research into active ingredients… is expensive.
In addition, nicotine has a very poor image… which impairs its marketability" adxs.org/en/page/192/ni…