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Maybe someone active in the research can comment? I feel like all of these comments are just conjecture/anecdotal and don't really get to the meat of this question of "progress" and the future of LLM's
There are real and obvious improvements in the past few model updates and I'm not sure what the disconnect there is. Maybe it's that I do have PhD level questions to ask them, and they've gotten much better at it. But I suspect that these anecdotes are driven by something else. Perhaps people found a workable prompt strategy by trial and error on an earlier model and it works less well with later models. Or perhaps t…
In the last year, things like "you are an expert on..." have gotten much less effective in my private tests, while actually describing the problem precisely has gotten better in terms of producing results. In other words, all the sort of lazy prompt engineering hacks are becoming less effective. Domain expertise is becoming more effective.
Earlier quoted context omitted.
> LLMs aren't good at being search engines, they're good at understanding things. LLMs are literally fundamentally incapable of understanding things. They are stochastic parrots and you've been fooled.
What do you call someone that mentions "stochastic parrots" every time LLMs are mentioned?
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LLMs aren't good at being search engines, they're good at understanding things. Put an LLM on top of a search engine, and that's the appropriate tool for this use case. I guess the problem with LLMs is that they're too usable for their own good, so people don't realizing that they can't perfectly know all the trivia in the world, exactly the same as any human.
> LLMs aren't good at being search engines, they're good at understanding things. LLMs are literally fundamentally incapable of understanding things. They are stochastic parrots and you've been fooled.
Earlier quoted context omitted.
> LLMs aren't good at being search engines, they're good at understanding things. LLMs are literally fundamentally incapable of understanding things. They are stochastic parrots and you've been fooled.
We're talking about a stochastic parrot which in many circumstances responds in a way which is indistinguishable from actual understanding.
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Really? Open-AI says PhD intelligence is just around the corner!
I don't think they mean "knowledge" when they talk about "intelligence." LLMs are definitely not knowledge bases. They can transform information given to them in impressive ways, but asking a raw (non-RAG-enabled) LLM to provide its own information will probably always be a mistake.
My mom told me yesterday that Paul Newman had massive problems with alcohol. I was somewhat skeptical, so this morning I asked ChatGPT a very simple question: "Is Paul Newman known for having had problems with alcohol?" All of the models up to o3-mini-high told me he had no known problems. Here's o3-mini-high's response: "Paul Newman is not widely known for having had problems with alcohol. While he portrayed charact…
Does the as yet unwritten prequel of Idiocracy tell the tale of when we started asking Ai chat bots for facts and this was the point of no return for humanity?
I've used gemini 2.5 this weekend with aider and it was frighteningly good. It probably depends a lot on what you are using them for, and in general, I think it's still too early to say exactly where LLMs will lead us.
Yep, and what they are going in cursor either the agentic stuff is really game changing. People who can’t recognize this intentionally have their heads in the sand
1) AI undoubtedly has utility. In many agentic uses, it has very significant utility. There's absolute utility and perceived utility, which is more of user experience. In absolute utility, it is likely git is the single most game changing piece of software there is. It is likely git has saved some ten, maybe eleven digit number in engineer hours times salary in how it enables massive teams to work together in very seamless ways. In user experience, AI is amazing because it can generate so much so quickly. But it is very far from an engineer. For example, recently I tried to use cursor to bootstrap a website in NextJS for me. It produced errors it could not fix, and each rewrite seemed to dig it deeper into its own hole. The reasons were quite obvious. A lot of it had to do with NextJS 15 and the breaking changes it introduces in cookies and auth. It's quite clear if you have masses of NextJS code, which disproportionately is older versions, but none labeled well with versions, it messes up the LLM. Eventually I scrapped what it wrote and did it myself. I don't mean to use this anecdote to say LLMs are useless, but they have pretty clear limitations. They work well on problems with massive data (like front end) and don't require much principled understanding (like understanding how NextJS 15 would break so and so's auth). Another example of this is when I tried to use it to generate flags for a V8 build, it failed horribly and would simply hallucinate flags all the time. This seemed very likely to be (despite the existence of a list of V8 flags online) that many flags had very close representations in vector embeddings, and that there was almost close to zero data/detailed examples on their use.
2) In the more theoretical side, the performance of LLMs on benchmarks (claiming to be elite IMO solvers, competitive programming solvers) have become incredibly suspicious. When the new USAMO 2025 was released, the highest score was 5%, despite claims a year ago that SOTA when was at least a silver IMO. This is against the backdrop of exponential compute and data being fed in. Combined with apparently diminishing returns, this suggests that the gains from that are running really thin.