[tshaddox]

If you conceptualize this as “there is an appropriate amount of brevity for each situation” then it would be expected for a better model to use different amounts of brevity if it gets better at determining the appropriate amount.

My view is that popular models by default output wildly excessive amounts of prose for nearly every use case, so if this changes in a new model that’s a pure win.

[ComputerGuru]

> wildly excessive amounts of prose

Not just prose. I think this is part of the reason why you see ridiculous code with insane error handling and type checking even for impossible cases.

[andai]

This is one reason I switched back to Claude after testing various alternatives a few months ago. Claude ended up writing much more elegant code.

Although I was surprised that I could get very Claude like results from Chinese models though by just telling it to make the code elegant.

Reminds me of the old days with art AI where you had to put "+good -bad" in the prompt because otherwise it would assume you just wanted random quality outputs, because it had been trained on random quality inputs...

[daveguy]

The models don't get better, except when a new one is released. Their performance depends solely on the model training before release and how well you curate the context you feed it. That's it. Contrary to popular belief these things are not intelligent.

[coldtea]

>The models don't get better, except when a new one is released. Their performance depends solely on the model training before release and how well you curate the context you feed it. That's it.

Not quite. The hosting side can change reasoning budgets (or re-assign what terms like "high" means), temperature and other decoding parameters, output length limits, finetune internal "hidden" prompt, latency optimizations, finetune attention algorithms, even change quantization - all still serving as the same model.

We know (or suspect) Anthropic frequently nerfs models while keeping their name and version the same.

[daveguy]

Right. They can do all those things. And none of that will make it smart or able to learn new things. The underlying model is just an llm. But judging from the downvotes, it seems AI folks get upset when someone talks honestly about their precious piles of matrix multiplication.

[sureMan6]

Might bother you to use anthropomorphic terminology like smart and learning but they are capable of producing work that traditionally required human intelligence and the whole point of gpt 3 was the ability to "learn", you can give it an example of an invented brand new coding language and it can write working code in that language

[kmacdough]

Context is not the same as learning. It's easy to conflate because they're tightly coupled in our brains.

The underlying structure and tuning of the LLM are entirely unchanged by context. It merely affects the attention and activation of the network. The LLM will not be able to work with this hypothetical new language unless it is in context. This does not fit the computational meaning of learning.

Smart is not a well defined term. Nor is it's general idea formally understood. Use it freely, but you won't be saying anything meaningful unless you define your usage.

[DiogenesKynikos]

The LLM is the model + context. The output depends on both.

You're making an artificial distinction. The LLM sees a new programming language for the first time, and can immediately code in it. That's learning by any reasonable definition.

If you go past the context window, it forgets, which is a limitation of current LLMs. But as long as it learned how to code in the new language within its context window, it has gained that new ability.

[xylenox]

Yep, people always forget that early LLMs were sold as "Zero Shot Learning".

[kmacdough]

Sold as learning, but that was a marketing term, not a technical one. From a technical perspective, the LLM is not learning. Only reacting based on its original training.

You might argue that the systems we've built around them are learning in a way, as they strategically condense and save artifacts from past interactions to pass into the LLMs context. But the LLM itself, which is the source of the intelligence, is not learning. It remains entirely unchanged throughout inference. This difference may seem trite, but it has significant impacts over the long term behavior.

[Nevermark]

Intelligence can operate without learning. At a minimum inference and learning don’t need to be co-concurrent.

Not disagreeing with your point, but your terminology muddies your point.

But your point doesn't acknowledge that even with inference, there is a lot of room to tune the calculations. Multiple models, quantization tradeoffs are just the most obvious examples. Every architecture can be adjusted to increase intelligence/watt or other measure, even without further training.

[jatora]

No thats probably because you misread what you were replying to and your comment was out of left field. They didnt imply models get better intra-releasally at all.

[coldtea]

You used the word "smart" now, whereas on the comment I replied to, you said "better".

Tuning those can definitely make a model respond better or worse.

So your claim (quoting 100% as written) that "Their performance depends solely on the model training before release and how well you curate the context you feed it" is wrong. Hence the downvotes.

Doesn't matter if LLMs are to be considered intelligent or not for the claim to be wrong.

> But judging from the downvotes, it seems AI folks get upset when someone talks honestly about their precious piles of matrix multiplication.

Often yes. In this case, it's more like they get upset when someone says something factually wrong, and then defensively changes the goalposts.

[daveguy]

> Often yes. In this case, it's more like they get upset when someone says something factually wrong, and then defensively changes the goalposts.

Oh give me a break. Show me one example of 1) any knob twisting that makes the underlying model better. or 2) any example of the AI providers twisting those knobs to do anything other than degrade performance for their own bottom line or safety.

The current post says: "it would be expected for a better model to use different amounts of brevity if it gets better at determining the appropriate amount."

When no, the model cannot "get better". It doesn't determine any appropriateness of response realtime except for the weights baked into it from the beginning and whatever context it can muster. If you cram enough guidance that it doesn't decide to ignore maybe you can make it more brief. But it (the model) can do none of those things.

LLM models are literally stupid by design.

[dahart]

Why did you drop the first half of the sentence in your quote? The qualification there is important context for the part you did quote. And why are you talking about “better” within a model, when the sentence you quoted was talking about 5.6 vs 5.5? The post you’re referring to did not suggest a single model could “get better”. You’ve made some incorrect assumptions.

Your comments are conflating multiple kinds of “smart” and “better”. You’re right that if all the inputs are exactly the same, it takes a new model to improve (ignoring non-determinism). But the knobs and context and harness change the inputs, and they do improve output, contrary to your claim. You’re failing to capture the distinction between what the model itself does and how the harness can boost the model’s performance. It is legitimately valid and fair to call improved performance “better”, no matter where it comes from.

This all gives me the feeling you might not have experience with or understand what’s happening in today’s harness development, and the degree to which it may be as important as the weights. There are in fact a lot of things you can do to improve a model’s performance on tasks & benchmarks, without changing the model weights. @coldtea mentioned a bunch, but the harness feedback loop, internal prompts, system prompts, skills, and requests for a model to try harder, and verify and validate it’s output all lead to improved performance, all without retraining.

I agree LLMs are stupid; they’re statistical token predictors. But somehow statistical token prediction is amazing and works much better than we imagined. The talking points about LLMs being stupid token predictors are fading now because they lack explanatory power for how good the models have become. The big surprise here isn’t about LLMs. It’s about language, and how much “thinking” and intelligence is contained in language. We don’t have a good grasp on where the line is between language and intelligence. LLMs have crushed the Turing Test into dust, and yet we don’t consider them intelligent. They often appear to understand what you ask thoroughly, can re-state it in different words, they can correct your misunderstandings or add nuance you didn’t see. All this because that’s what humans do and LLMs talk like humans.

[blackqueeriroh]

> If you cram enough guidance that it doesn't decide to ignore maybe you can make it more brief.

You are now anthropomorphizing the model yourself.

[coldtea]

>Oh give me a break. Show me one example of 1) any knob twisting that makes the underlying model better.

I mentioned several.

You're now once again changing goalpoasts to say you meant the underlying model, not the overall llm performance, even though you explicitly wrote: "Their performance depends solely on the model training before release and how well you curate the context you feed it".

So, the context curation was relevant (meaning you didn't constrain your claim to the underlying model), but now somehow all the additional tunables aren't relevant (because suddenly you're just talking about the model).

End of discussion.

[DiogenesKynikos]

I can imagine an AI insulting humans in the same way:

"The underlying model is just a biological neutral network. It seems you carbonoids get upset when someone talks honestly about synapses and neuron firing."

[daveguy]

Neural plasticity is real, and something LLMs are incapable of. So sorry.

[dahart]

True for today’s static models during inference. Not true for self-supervised learning, not true during training or fine-tuning, of course. Ignores that LLMs might start continuous training in the future - there’s no fundamental or technical constraint that prevents LLM ‘plasticity’. And ignores that accumulating context/memories/skills/etc affects performance and might count as a valid analogy to what many people loosely call ‘neural plasticity’, which is sometimes casually mistaking knowledge for network modification.

[avarun]

This has absolutely nothing to do with the comment you replied to.

[Cycl0ps]

>The models don't get better, except when a new one is released.

My brother in Christ this entire thread is talking about the new model that was released

[daveguy]

It was edited. Original talked about the model learning. Glad they managed to clarify. Because the models are quite literally stupid.

[dahart]

> the models are quite literally stupid.

You’re arguing via reductionism, and failing to explain the outcomes and emergent properties of the “stupid” system. Humans are made of atoms that are quite literally stupid, so by all means, explain our intelligence and why it’s different than LLMs. (I’m not claiming LLMs are intelligent, BTW, I just don’t think your claim helps nor believe that you can fix it.)

https://en.wikipedia.org/wiki/Reductionism#Definitions

[daveguy]

Seriously? Supid is literally incapable of learning. That is the underlying model. It does not change, therefore it does not learn. LLM models are quite literally stupid. No one gets a new model no matter how much they yell at it in the context.

[dahart]

It’s as if you didn’t read or respond to my comment. LLM models are capable of learning… it’s called training. Models do change during training. You seem to be fixated on today’s inference, perhaps?