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Top people in LLMs
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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.
Their words
Another manifestation of the lack of sufficiently abstract, formal reasoning in LLMs is the way in which performance often fall apart as problems are made bigger.
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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