LW - LLM Applications I Want To See by sarahconstantin

The Nonlinear Library: LessWrong

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Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: LLM Applications I Want To See, published by sarahconstantin on August 20, 2024 on LessWrong.
I'm convinced that people who are interested in large language models (LLMs) are overwhelmingly focused on general-purpose "performance" at the expense of exploring useful (or fun) applications.
As I'm working on a personal project, I've been learning my way around HuggingFace, which is a hosting platform, set of libraries, and almost-social-network for the open-source AI community. It's fascinating, and worth exploring even if you're not going to be developing foundation models from scratch yourself; if you simply want to use the latest models, build apps around them, or adapt them slightly to your own purposes, HuggingFace seems like the clear place to go.
You can look at trending models, and trending public "spaces", aka cloud-hosted instances of models that users can test out, and get a sense of where the "energy" is. And what I see is that almost all the "energy" in LLMs is on general-purpose models, competing on general-purpose question-answering benchmarks, sometimes specialized to particular languages, or to math or coding.
"How can I get something that behaves basically like ChatGPT or Claude or Gemini, but gets fewer things wrong, and ideally requires less computing power and and gets the answer faster?" is an important question, but it's far from the only interesting one!
If I really search I can find "interesting" specialized applications like "predicts a writer's OCEAN personality scores based on a text sample" or "uses abliteration to produce a wholly uncensored chatbot that will indeed tell you how to make a pipe bomb" but mostly…it's general-purpose models. Not applications for specific uses that I might actually try.
And some applications seem to be eager to go to the most creepy and inhumane use cases. No, I don't want little kids talking to a chatbot toy, especially. No, I don't want a necklace or pair of glasses with a chatbot I can talk to. (In public? Imagine the noise pollution!) No, I certainly don't want a bot writing emails for me!
Even the stuff I found potentially cool (an AI diary that analyzes your writing and gives you personalized advice) ended up being, in practice, so preachy that I canceled my subscription.
In the short term, of course, the most economically valuable thing to do with LLMs is duplicating human labor, so it makes sense that the priority application is autogenerated code.
But the most creative and interesting potential applications go beyond "doing things humans can already do, but cheaper" to do things that humans can't do at all on comparable scale.
A Personalized Information Environment
To some extent, social media, search, and recommendation engines were supposed to enable us to get the "content" we want.
And mostly, to the extent that's turned out to be a disappointment, people complain that getting exactly what you want is counterproductive - filter bubbles, superstimuli, etc.
But I find that we actually have incredibly crude tools for getting what we want.
We can follow or unfollow, block or mute people; we can upvote and downvote pieces of content and hope "the algorithm" feeds us similar results; we can mute particular words or tags.
But what we can't do, yet, is define a "quality" we're looking for, or a "genre" or a "vibe", and filter by that criterion.
The old tagging systems (on Tumblr or AO3 or Delicious, or back when hashtags were used unironically on Twitter) were the closest approximation to customizable selectivity, and they're still pretty crude.
We can do a lot better now.
Personalized Content Filter
This is a browser extension.
You teach the LLM, by highlighting and saving examples, what you consider to be "unwanted" content that you'd prefer not to see.
The model learns a classifier to sort all text in yo...