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LLMs Are Complicated Now
- deleted 4mo ago[deleted]
- charcircuit 4mo agoWhy didn't this author compare Llama 3 with GLM 5.2 (released 1 week ago) which is a more standard attention based LLM? To compare 2 separate families of LLMs and then pointing out that they are different is not a surprising result and detracts from the point the author is trying to make. https://sebastianraschka.com/llm-architecture-gallery/?compare=llama-3-8b%2Cglm-5-2#architecture-diff-tool https://sebastianraschka.com/llm-architecture-gallery/?compa... If you look at it, the diagrams are very similar, but the main differences are that the feedforward is replaced with a MoE (router to multiple feedforwards) and the model has a different attention implementation.
- christopherwxyz 4mo agoIt’s written by AI.
- alecco 4mo agoGrammarly and GPTZero say 0% AI.
- lproven 4mo ago[[citation needed]] I am a professional writer and have been for over 30 years. (I do not use any form of LLM ever.) This means I read a lot. This also means that I have 30+ years of experience of readers not understanding what I wrote, or not getting further than the title, or not getting the main message, or inverting it in their heads, or inserting their own message and then complaining when I diverge, and an endless list of Ways People Do Not Get It. I am also a trained TESOL teacher. Ability to capture gist is a skill we test for and measure, and many, maybe the majority, of native speakers don't have it and don't know. In recent years I constantly see people going "this is written by AI" and I have yet to see a single of of them able to coherently prove their point. It's all just feelings and hunches. So I am calling you on this: How do you know? Show your working. Demonstrate your case.
- skydhash 4mo agoI don’t think TFA is written by AI. But AI written pieces do have a certain feeling. A sort of saccatto in the succession of ideas that does not feel natural. They emphasize certain points, and you as a reader, you just wonder why is that. There is the “This thing, not just that thing”. There are also the three successive propositions (mostly in one sentences) to accentuate an idea and “Negation. Strong positive idea in the same direction”. In general try reading one (vocally) to yourself and it will feel really weird.
- jghn 4mo agoJust like em-dashes, some people have always done these though. Why are they penalized with immediate AI slop witch hunts? The LLMs didn't come up with these tics out of thin air.
- simion314 4mo ago>The LLMs didn't come up with these tics out of thin air. LLMs were trained with a lot of synthetic data to transform them from a complete this text into a chatbot, I suspect that this tons of synthetic data that forces the LLM to answer questions into a specific ways also forced them to have this "synthetic/robotic" language. Claude users would have noticed the "belter and suspenders" phrase just started popping out after an update and I am sure is nto because lots of developers used it in their blogs and Anthropic scrapped those blogs in that update.
- skydhash 4mo agoYou have to start with the reason there’s a witch hunt. I love reading. I read books (novels) almost every day and I’m almost always perusing a textbook pr an article for my jobs and my hobbies. The signal/noise for entertainment or information was fairly high, then come LLM tools. You start to be interested by the title of an article or a book cover, and then you start reading it and it’s just vapor. Nothing tangible to be gained. It’s like buying something expensive and finding out a cheap trinket under the wrapping. After a couple of times, you will develop a certain kind of heuristics for this kind of texts. It will not be perfect and will have some false positives, but that’s the only way to keep your sanity.
- jddj 4mo agoHighly doubtful
- rizky05 4mo ago[dead]
- Philpax 4mo agoI am _very_ familiar with Claudish, and to some extent, the other AIs' writing styles. This article is human-written and features human writing quirks. The very first sentence > Back in 2022 and 2023 there were two big branches of machine learning happening at Meta. is unmistakably human. That's not how a LLM would phrase this sentence, and if it did, it would have put a comma after 2023.
- fightforcause 4mo agoYou can just prompt an llm to make causal grammar mistakes
- jkaplowitz 4mo agoLeaving out that comma is not a grammar mistake. The comma slightly changes the feel of the sentence, but it's not wrong to include or omit it. But yeah, I agree with the other commenter that AI would be less likely than a human to omit the comma.
- xyz100 4mo agoAn adverbial clause at the front of a sentence should have a comma? Or is that not what it is?
- jkaplowitz 4mo agoAdverbial clauses at the front of a sentence don't always have to have a comma. It's often taught this way to new English language learners or in style guides that want to keep things as unambiguous as possible because including the comma in that case is never wrong. But it's also sometimes optional. Here is the Chicago Manual of Style's Q&A on commas: https://www.chicagomanualofstyle.org/qanda/data/faq/topics/Commas.html https://www.chicagomanualofstyle.org/qanda/data/faq/topics/C... One of the answers cites section 6.34 of the Chicago Manual of Style as follows: "Although an introductory adverbial phrase can usually be followed by a comma, it need not be unless misreading is likely. Shorter adverbial phrases are less likely to merit a comma than longer ones." For the example we're discussing, "Back in 2022 or 2023", my personal instinct as a university-educated native US English speaker would be to include or omit the comma primarily depending on how much emphasis I want to put on the timing of my statement. I also know that I tend to write overly long sentences with too many commas, so sometimes I'd intentionally counteract my own tendency and limit the complexity of the sentence by removing a comma like that if the sentence still seems good without it. Other times I'd split the sentence into two, but my sentences rarely get as staccato as AI-written ones.
- lproven 4mo ago> If you look at it, the diagrams are very similar, The page links to the same site you do. No wonder it is similar -- the source is the same!
- charcircuit 4mo agoThe source is the same in the original article too. He is using a different diagram from the same site on the right to justify his point on how much more complicated things have become.
- alecco 4mo agoYeah, not a great apples-to-apples comparison. I think the point stands: MoE, a myriad of complex attention approaches, shared layers, you name it. And making it all work together well is a huge trial-and-error pain even for small models, never mind getting to efficient hardware utilization.
- segmondy 4mo agoThe author is correct, the model architecture is now much more complicated. You can see this if you use llama.cpp and follow the project. The earlier models were always fully implemented. Yet with more contributors, as of today tons of latest models only have partial implementation. DeepSeekv3.2 isn't fully implemented, same with KimiK2.6, GLM5.2+, DeepSeekv4 has no implementation, MiniMaxM3 not supported yet, Hy3-preview no implementation. The latest models are just bare bones to run with lots of support missing for the advanced features.
- KerrAvon 4mo agoindeed, there's even a (pretty solid) custom server just for DS4 https://github.com/antirez/ds4 https://github.com/antirez/ds4 -- works very well on high-RAM Macs
- alfiedotwtf 4mo agoI don’t k ow what I’m doing wrong. Everyone says ds4 is faster than a lot of models around the same size, but I’m getting 2t/s with DSv4 vs 12t/s with Minimax 2.7 (16Gb 5080 + 16gb 5060ti + 128gb ram).
- enduser 4mo agods4 is optimized for systems with unified memory. it works best on apple silicon with 96GB+ of RAM
- alfiedotwtf 4mo agoah! ok, now that makes sense. Thanks for that.
- charcircuit 4mo agoThe architecture is not much more complicated. And llama.cpp not implementing something is more likely an issue of its business model and financial incentives than it being due to raw complexity.
- embedding-shape 4mo ago> Why didn't this author compare Llama 3 with GLM 5.2 (released 1 week ago) which is a more standard attention based LLM? To compare 2 separate families of LLMs and then pointing out that they are different is not a surprising result and detracts from the point the author is trying to make. The entire point of the comparison is that LLMs look vastly different today than before. Comparing more similar LLMs would detract from the point I thought the author was trying to make.
- charcircuit 4mo agoIt is misleading the reader since most current LLMs look the same as before. It is cherry picking an example to make a point when it's not necessary at all to make the argument he is trying to make.
- embedding-shape 4mo ago> It is misleading the reader since most current LLMs look the same as before. But most of them do not? They do look vastly different from the earlier incarnations of GPT and Llama.
- charcircuit 4mo agoCan you provide an example? I provided an example of a week old model looking mostly the same.
- cold_harbor 4mo ago[flagged]
- jordanb 4mo agoIt's the bitter-lesson to feature-engineering lifecycle. When a technique or technology is new people are making massive gains by just applying it to some use case, or gathering more data for training, or giving it more resources. As time goes on those "bitter lesson" gains start to hit the shallow part of the logistic curve and companies have to start investing more and more effort into engineering for each small, incremental gain.
- vjvjvjvjghv 4mo ago[dead]
- pezo1919 4mo agoWell put, thanks.
- sdenton4 4mo agoI got a very different message from this, actually much closer to the problem of incumbent advantage. The known-good thing has been heavily optimized for performance, making it much harder for new technologies to prove that they are better. This is similar to the problem of gas vs electric engines - we had a century of optimization and ecosystem development around gas engines, which creates an uphill battle for electric motors even though they are (eventually) superior on every way /except/ having that massive ecosystem. The problem isn't as bad here, because software is much more flexible than hardware, and scaling laws give a reasonable way to try things out at smaller scale before going whole hog.
- lukeschlather 4mo agowhat is the known-good thing? The whole point is that LLMs were not optimized at all, they got better results than older ML algorithms just because they are able to use all of the GPU, where older algorithms are designed for 10yo GPUs and can't make use of modern GPUs. But now you do in fact have to optimize, to the point that transformers look a lot more complicated than "attention is all you need."
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- truvem 4mo ago[flagged]
- tossandthrow 4mo agoThat does not seem to be related to llms? It is more about the harness that utilizes them, right?
- ffsm8 4mo agoIf you think statefull LLMs would be easier to handle then stateless... Then I think you haven't done a lot of software engineering
- zsyllepsis 4mo agoMaybe a charitable reading of the parent comment, but my interpretation of it was that while the _models_ are stateless, modern deployments of these models for inference rely on state. For example, tiered pricing for cached context relies on state, even if the models don’t.
- zahlman 4mo agoFor that matter, the agent harness accumulating "chat history" is state.
- random3 4mo agoThis. lol. If you think state makes things easier you're in for a big surprise.
- vivzkestrel 4mo ago- with all due respect, from a ux perspective, could you kindly add a page where i can see just the titles of all your blog posts - https://ianbarber.blog/blogroll https://ianbarber.blog/blogroll - https://ianbarber.blog/archive https://ianbarber.blog/archive - https://ianbarber.blog/blog https://ianbarber.blog/blog - https://ianbarber.blog/posts https://ianbarber.blog/posts - none of the above links work - i really dont want to scroll 200 pages just to see what your blog articles are
- smasher164 4mo agolol yeah I guess the best move right now is to fetch their /feed and iterate through <post>s
- MarkSweep 4mo agoThere is https://ianbarber.blog/feed https://ianbarber.blog/feed
- vivzkestrel 4mo agofirefox directly downloads it, i cant see what it is without downloading a file
- aesthesia 4mo ago> Claude Telenovela Nice, hadn't seen this one before.
- physix 4mo agoFor someone like me who's never done any hands-on work in ML, the blog is really hard to understand. Whoosh, over my head. But, I think the underlying problem is that we don't understand how this sh*t works. So, it's just an empirical, iterative mess. Like physics in the the years shortly before relativity and quantum mechanics.
- kaycey2022 4mo agoMy read was: "The first 90% of the work takes the first 10% of the time, and the remaining 10% of the work takes the other 90% of the time." And that we are now squarely in the remaining 10% of the work.