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Top people in LLMs
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From one piece Product Evals in Three Simple Steps 2 beliefs, in the piece's order there
Their words
The benchmark is human performance, not perfection. We sometimes get requirements for 90%+ accuracy.
korrents.com
The main advantage of an LLM evaluator over human annotators is scalability, not higher accuracy.Their words
In my opinion, the true benefit isn't higher accuracy than human annotators-it's scalability.
From one piece Andrej Karpathy — “We’re summoning ghosts, not building animals” 2 beliefs, in the piece's order there
Their words
so I think there's there's an interesting point here because I do believe coding is like the perfect first thing for uh for a for uh these LLMs and uh agents and that's because coding has always fundamentally uh worked around text.
Their words
the reason that I think this is kind of tricky is quite subtle. And it's the fact that anytime you use an LLM to assign a reward, those LLMs are giant things with billions of parameters and they're gameable.
Their words
You know, interestingly, LM themselves are quite bad at playing chess. Like, they hallucinate moves. They look at patterns, right? They're they're very good at pattern recognition, but not so good at going super super super deep on a specific chess thing.
From one piece Richard Sutton – Father of RL thinks LLMs are a dead end 3 beliefs, in the piece's order there
Their words
We don't we don't really know what information they had prior. We are we have to guess because they've been fed so much. This is one reason why they're not a good way to do science. Uh it's just so uncontrolled, so unknown.
Their words
The scalable method is you learn from experience. Um you uh you you try things, you see what you see what works. No one no one has to tell you. First of all, you have a goal. So without a goal, uh there's no sense of right or wrong or better or worse. So large language models are trying to get by without having a goal or a sense of better or worse. That's just, you know, it's exactly starting in the wrong place.
Their words
reinforcement learning is about understanding your world whereas large language models are about mimicking people doing what people say you should do. They're not about figuring out what to do.
korrents.com
Almost every major figure in AI has now arrived at the critique of large language models he began making in 2019.Their words
But one by one, almost every major thinker in AI has come around to the critique of LLMs that I began presenting in 2019.
Their words
uh common sense meaning the ability to make inferences about what might happen uh that are reasonable guesses but that do not require you to experience that mistake and learn and learn from it in advance that's tremendously important and that's something that we basically had no idea how to do uh about 5 years ago but now uh you we can actually use LLMs and VLMs ask them questions and they will make reasonable guesses
From one piece Why Everyone Is Wrong About AI (Including You) | Benedict Evans 3 beliefs, in the piece's order there
Their words
I think it's actually the opposite which is that everyone's kind of using the same data which is you need such an enormous amount of generalized text that the amount that Google has or that meta has is not actually enough to move to be a kind of fundamental difference in what you can train with.
Their words
You see this for the music now. You can generate new music. It could generate new stuff that you wouldn't know. For an LLM variance is bad. originality is is is a lower score. So what's the feedback loop for original but good?
korrents.com
Large language models have no network effect, and remembering a user is a switching cost rather than one.Their words
there's no apparent equivalent in LLM right now there's no reason why the LMS get better because more people use them now that may come you have that open AI and people have doing memory where it remembers what else you've asked, but that seems more like a switching cost than a network effect.
Their words
One of my favorite papers about prompt injection is Design Patterns for Securing LLM Agents against Prompt Injections
Usesrcmnd.app
MochiTheir words
I initially started making Anki (well, Mochi) cards to keep track of Google DeepMind's LLM lineage—Gopher, Chinchilla, Gato, PaLM, Sparrow, Meena, LaMDA, Bard, Gemini, etc.
Their words, before
This means that working on neural networks is NOT getting us closer to AGI, except indirectly.
Their words now
It’s become very difficult for me to maintain the belief in the stupidity of ChatGPT when every time I laugh at it, it ends up ridiculing me 6 months later.
korrents.com
LLM-based evaluation methods are more reliable and nuanced than traditional automated metricsTheir words
This is why model-based evaluation is increasingly popular-it offers more reliable and nuanced evals than traditional metrics.
korrents.com
The success of large language models vindicates semantic inferentialism over symbolic, rule-based approaches to AI.Their words
The success of LLMs can thus be seen as vindicating semantic inferentialism against earlier, symbolic approaches to AI that tried and failed to explicate the rules of ordinary language using formal logic.
From one piece A knockout blow for LLMs? 4 beliefs, in the piece's order there
Their words
Worse, as the latest Apple papers shows, LLMs may well work on your easy test set (like Hanoi with 4 discs) and seduce you into thinking it has built a proper, generalizable solution when it does not.
Their words
What the Apple paper shows, most fundamentally, regardless of how you define AGI, is that LLMs are no substitute for good well-specified conventional algorithms.
Their words
But this particular approach has limits that are clearer by the day.
korrents.com
Anyone who thinks large language models are a direct route to transformative AGI is kidding themselves.Their words
But anybody who thinks LLMs are a direct route to the sort AGI that could fundamentally transform society for the good is kidding themselves.
Their words
the text embeddings across LLMs appear to largely converge on a "universal geometry" despite differing architectures, parameter counts, and training sets.
Their words, before
The problem is that the conversational interface is potent and that the AI is trained on a lot of human text input which unfortunately is probably enough to do real damage if that conversational interface is hooked up with something that has real world consequences.
Their words now
While all this is happening, I’ve found myself reflecting a lot on what AI means to the world and I am becoming increasingly optimistic about our future. It’s obvious now that we’re undergoing a tremendous shift.
From one piece My AI Skeptic Friends Are All Nuts 2 beliefs · fly.io
Their words
All progress on LLMs could halt today, and LLMs would remain the 2nd most important thing to happen over the course of my career.
Their words
LLMs can write a large fraction of all the tedious code you’ll ever need to write. And most code on most projects is tedious. LLMs drastically reduce the number of things you’ll ever need to Google.
Lovedrcmnd.app
Anthropic prompt engineering documentationTheir words
I still think Anthropic have the best prompting documentation of any LLM provider.
Their words
Unless you’re an extreme power user, asking AI questions every day is still a rounding error on your total electricity footprint.
What's the carbon footprint of using ChatGPT?hannahritchie.substack.com
korrents.com
Large language models cannot reliably self-correct their own mistakes without external feedback.Their words
this self-correction capability turns out to not exist intrinsically among LLMs and does not easily work out of the box, due to various failure modes
From one piece ThePrimeagen: Programming, AI, ADHD, Productivity, Addiction, and God | Lex Fridman Podcast #461 3 beliefs, in the piece's order there
Their words
I think there's this whole idea, I call it a denial of attention. I think there's an entire attack vector that's going to be happening. We're using LLMs to generate fake bug reports, fake all these things to just actually effectively to demotivate and hurt open source maintainers.
korrents.com
Without hard skills of your own, your ceiling as a programmer is whatever the LLMs happen to be capable of.Their words
And so if you don't have those hard skills and you're not ultimately the driver at the end of the day, you're going to really find some hard times, and your ability to progress will be directly bound to how good the LLMs are.
Their words
It doesn't actually care about the craft. But when you work with an intern or you work with somebody else, they care. When they factor something, they actually go over and go, "Oh yeah, this is actually kind of bad. I'm going to come back to that." They finish this, they go back over here and they make this even better. They actually care about the thing itself.
Likedrcmnd.app
llmTheir words
This is a command line tool with plenty of plugins that lets you prompt different models. Think of it as a command-line version of Open WebUI. It’s particularly useful for quick scripting and basic automation.
korrents.com
AI tooling and interface design lags behind the underlying models' actual capabilities.Their words
Overall, though, this experience reinforced my belief that the tooling and interface design around LLMs is lagging way behind the actual capabilities, and is an area of active experimentation and development, even aside from any future model improvements.
From one piece How I program with LLMs 2 beliefs, in the piece's order there
Their words
One thing humans appear to be much better than LLMs at (as of January 2025) is not getting distracted.
Their words
So I foresee a world with far more specialized code, with fewer generalized packages, and more readable tests.
Their words
There’s still plenty to worry about with respect to the environmental impact of the great AI datacenter buildout, but a lot of the concerns over the energy cost of individual prompts are no longer credible.
Recommendsrcmnd.app
The Rattle BagTheir words
The Rattle Bag edited by Seamus Heaney and Ted Hughes - In an age of LLM-generated poetry and machine learning curation (even coming from yours truly sometimes), it's refreshing to have real people who love poetry curate it and show you what you need to read to touch grass.
korrents.com
Large language models will not get to real agency, because a mind has to be embedded in a world through a body.Their words
But they said, "Look, in the end, actually the only way to actually get a real brain in the VAT is actually to have a brain in a body." And it could be a robot body, but you still need a brain in the body. So I don't think LLMs will get there because they can't. You really need to be embedded in a world, at least that's the E-four idea.
Their words, before
This "memorize, fetch, apply" paradigm can achieve arbitrary levels of skills at arbitrary tasks given appropriate training data, but it cannot adapt to novelty or pick up new skills on the fly (which is to say that there is no fluid intelligence at play here.)
Their words now
OpenAI's new o3 model represents a significant leap forward in AI's ability to adapt to novel tasks. This is not merely incremental improvement, but a genuine breakthrough, marking a qualitative shift in AI capabilities compared to the prior limitations of LLMs.
korrents.com
A language model is not using language at all, because language requires an intention to communicate.Their words
But a large language model is not a writer; it’s not even a user of language. Language is, by definition, a system of communication, and it requires an intention to communicate.
From one piece Extrinsic Hallucinations in LLMs 2 beliefs, in the piece's order there
korrents.com
Using supervised fine-tuning to teach a language model new knowledge risks increasing its hallucination rate.Their words
These empirical results from Gekhman et al. (2024) point out the risk of using supervised fine-tuning for updating LLMs' knowledge.
korrents.com
A language model avoids hallucinating only by being factual and admitting when it does not know the answer.Their words
To avoid hallucination, LLMs need to be (1) factual and (2) acknowledge not knowing the answer when applicable.
Their words
In Google, even though we call it 10 blue links, you get annoyed if you don’t even have the right link in the first three or four. The eye is so tuned to getting it right. LLMs are fine. You get the right link maybe in the 10th or ninth. You feed it in the model. It can still know that that was more relevant than the first.
Their words
I think large language models are kind of like the genetic system for language in some sense, where it’s allowing an archiving that’s highly dynamic.
Their words
What a large language model is trying to do is to predict text. That’s what it does. And it is leveraging the fact that we human beings for very good evolutionary biology reasons, attribute intentionality and intelligence and agency to things that act like human beings.
korrents.com
Retrieval-augmented generation is the most practical approach for reducing LLM hallucinations.Their words
Retrieval-augmented generation (RAG; Lewis et al., 2020), which conditions on the LLM's generation on retrieved documents is the most practical paradigm IMO.
Their words
recent LLMs are reaching the limits of text data online and repeating data eventually leads to diminishing returns
Their words
It's interesting to me that large language models in their current form are not inventions, they're discoveries. The telescope was an invention, but looking through it at Jupiter, knowing that it had moons, was a discovery.
From one piece EMNLP 2023 Primer 3 beliefs, in the piece's order there
Their words
In light of the increasing number of closed-source LLMs, it is important to continue to promote an open culture of sharing knowledge, data, and software, from which the NLP community has benefited greatly.
Their words
LLMs are still limited in non-English settings and that making LLMs more multilingual is an important direction.
Their words
Full fine-tuning of LLMs has become prohibitive and requires parameter-efficient methods instead.
Their words
Large language models are more or less generalizations of stuff that we have. There’s still breakthroughs in AI waiting to happen, and maybe they are happening and maybe they’ll be good, maybe not, but that’s not quite the same.
From one piece George Hotz: Tiny Corp, Twitter, AI Safety, Self-Driving, GPT, AGI & God | Lex Fridman Podcast #387 2 beliefs, in the piece's order there
Their words
I think things like MuZero and AlphaGo are so much more impressive because these things are playing beyond the highest human level. The language models are writing middle school level essays and people are like, wow, it’s a great essay.
Their words
I think future LLMs are going to be smaller, but are going to run looping on themselves and are going to have retrieval systems. And the thing about using a retrieval system is you can cite sources, explicitly.
korrents.com
Chatbots are a terrible interface for large language models, because a text box has no affordances.Their words
Hopefully I've convinced you that chatbots are a terrible interface for LLMs. Or, at the very least, that we can add controls, information, and affordances to our chatbot interfaces to make them more usable.
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