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LLM architecture comparison
- bravesoul2 1y agoThis is a nice catchup for some who hasn't been keeping up like me
- dmezzetti 1y agoWhile all these architectures are innovative and have helped improve either accuracy or speed, the same fundamental problem of generating factual information still exists. Retrieval Augmented Generation (RAG), Agents and other similar methods help mitigate this. It will be interesting to see if future architectures eventually replace these techniques.
- tormeh 1y agoTo me, the issue seems to be that we're training transformers to predict text, which only forces the model to embed limited amounts of logic. We'd have to find something different to train models on in order for them to stop hallucinating.
- lblume 1y agoModern neuroscience suggests that everything the human brain might be doing is basically a kind of predictive processing, i.e. hallucination based on inductive biases. I do not think this is the main bottleneck.
- bsenftner 1y agoI'm still thinking about how RAG being conceptually simple and easy to implement, why the foundational models have not incorporated it into their base functionality? The lack of that strikes me as a negative point about RAG and it's variants, because if any of them worked, it would be in the models directly and not need to be added afterwards.
- bavell 1y agoRAG is a prompting technique, how could they possibly incorporate it into the pre training?
- maleldil 1y agoCoT is a prompting technique too, and it's been incorporated.
- bavell 1y agoIIUC, CoT is "incorporated" into training by just providing better quality training data which steers the model towards "thinking" more deeply in its responses. But at the end of the day, it's still just regular pre training. RAG - Retrieval augmented generation - how can the retrieval be done during training? RAG will always remain external to the model. The whole point is that you can augment the model by injecting relevant context into the prompt at inference time, bringing your own proprietary/domain-specific data.
- bsenftner 1y agoWho says "during training"? RAG could be built into the functionality of the LLM directly - give it the documents you want it to incorporate, and it ingests them as a temp mini-fine tune. That would work just fine.
- impossiblefork 1y agoThese things with <think> and </think> tokens are actually trained using RL, so it's not like GSM8k or something like that where you just train on some reasoning. It's actually like QuietSTaR but with a focus on a big thought in the beginning and with more sophisticated RL than just REINFORCE (QuietSTaR uses REINFORCE).
- bsenftner 1y agoThe same way developers incorporate it now. Why are you thinking "pre-training", this is a feature of the deployed model: it ingests documents and generates a mini-fine tune right then.
- esafak 1y agoThe models can't tell when they shouldn't extrapolate and simply need more information. Which rules can be generalized and which ones can't. Why shouldn't a method `doWhizBang()` exist if there methods for all sorts of other things? When I was young, I once beamed that my mother was a good cooker. It made perfect sense based on other verbs, but I did not know that that word was already claimed by machines, and humans were assigned the word cooks. Decades later, I had the pleasure of hearing my child call me a good cooker...
- lblume 1y agoThis made me think – the fact that the underlying rule that nouns e.g. representing activities such as cooking can be formed from the corresponding verb via the suffix -er breaks in this case is just a historical / cultural artifact of languages, a type of completely unnecessary complication from the machines' standpoint. Maybe LLM hallucination might also partially be caused by this exception-based social modelling forced via our training data into all model architectures?
- deleted 1y ago[deleted]
- ethan_smith 1y agoSome newer architectures like DeepSeek-V2 and Llama 3.1 have actually shown significant factuality improvements through architectural changes alone, including improved attention mechanisms and training objectives specifically targeting hallucination reduction.
- Chloebaker 1y agoHonestly its crazy to think how far we’ve come since GPT-2 (2019), today comparing LLMs to determine their performance is notoriously challenging and it feels like every 2 weeks a models beats a new benchmark. I’m really glad DeepSeek was mentioned here, bc the key architectural techniques it introduced in V3 that improved its computational efficiency and distinguish it from many other LLMs was really transformational when it came out.
- southernplaces7 1y ago[flagged]
- strangescript 1y agoThe diagrams in this article are amazing if you are somewhere in between a novice and expert. Seeing all of the new models laid out next to each other is fantastic.
- webappguy 1y agoWould love to see a PT.2 w even what is rumored in top closed source frontier models eg. o5, o3 Pro, o4 or 4.5, Gemini 2.5 Pro, Grok 4 and Claude Opus 4
- DeveloperErrata 1y agoThis was really educational to me, felt at the perfect level of abstraction to learn a lot about the specifics of LLM architecture without the difficulty of parsing the original papers
- cindyllm 1y ago[dead]
- ajeet 1y agoThank you for taking the time to detail the differences - very educational and easy to read.
- krackers 1y agoAlso related https://epoch.ai/gradient-updates/how-has-deepseek-improved-the-transformer-architecture https://epoch.ai/gradient-updates/how-has-deepseek-improved-... and some sections of https://semianalysis.com/2025/07/11/meta-superintelligence-leadership-compute-talent-and-data/ https://semianalysis.com/2025/07/11/meta-superintelligence-l...
- TheDudeMan 1y ago[flagged]
- lcfcjs6 1y ago[dead]