Just use the LLM to make a thing that's actually good at what you need it to do.
This is the end state of most imagined use cases for “agentic” software IMO. Use the AI to build software, with a classifier or LLM as the expansion joint in awkward spots.
For example, a client want a classifier to decide to make a go/no go decision on roofing jobs based on weather. The end result doesn’t use LLMs at all, but does use a lot of other techniques including human feedback when it makes the wrong call.
(Now the work crew gets the decision at 5 AM instead of 5:30 and the guy making the decision doesn’t have to wake up at all.)
You have to know the right words to ask the LLM to make and train a classifier. You and I already know the words. But they are not actually that trivial to learn for an average joe, and people are lazy.
«We came full circle on the problem that also exists with Google Search, and why so many people don't get good results out of it.» [0] It is not that access to all information was hidden to anyone before, it was harder to find it, sure. Now it can be found easily and aggregated, however, it is not as simple as saying "Do X" and believe that X is correct. Recall the infamous Swing & Product Management meme? [1] The problem is the absence of information you provide, the LLM will have to make a guess of what you mean, much like the human. Likelihoods of this guess being correct depends on the domain and background. So yes, it'll likely build whatever you ask it (I do think we're there already), but if that matches what you intended to actually get is vastly different. You'll be settling with "Good enough" though and declare it working.
Others already pointed out that 6 months seem to be the constant of distance, irrespective of the date.
[0] - https://news.ycombinator.com/item?id=50063427#50063744
[1] - https://www.productftw.com/productftw-2-the-tire-swing-carto...
The corpus of code available to train LLMs on is rich in both volume and quality. The same can't be said for LLM prompts. Prompts only came into existence alongside LLMs themselves, so most of the prompts out there are actually LLM generated, and training on them causes every model to overfit to the same patterns.
LLMs can give clear and concise directives to other LLMs, but only for basic tasks. Beyond that, they frequently overstuff their prompts with too much prose and too much direction, and worse, they often fill them with subtle contradictions.
Of course, there's a spectrum here. Some models turn out to be better at this than others, and I suspect this is what differentiates Fable-type models from the rest, and why vendors have started keeping their models' reasoning confidential.
Ten years ago, in France, two doctors (one in a professional setting and the other during a private consultation) asked me about a wound I had on my shoulder; obviously I couldn't identify what it was, and they offered no suggestions, so I supposed it was not something important.
It took a visit to a cardiologist—for an entirely different reason—for him to suggest that I rapidly consult a dermatologist.
It was a carcinoma, and surgery removed it. If ChatGPT had existed back then, this carcinoma would have been removed several years earlier.
Automation doesn't make up for carelessness.
I agree that a good model will outperform an LLM, but the LLM is so much faster/easier to build. That’s the usual tradeoff.
People vastly underrate the patience of people to learn and setup a classifier. Jev is one API call. Training a good classifier is hard.
We have in the works a classifier for if a prospective customer will be long term profitable or not. This changes everything.
Same for annoying training jobs, with sufficient data you can probably let an LLM loose on it in autotrain mode and it will come back with a decent classifier.
With enough marketing you can sell anything. Also a decent way to make a product is to do the hard part for someone else.
I've been training classifiers for ten years, it used to be hard. Now it's hard to do really well but it's trivial to do well enough.
An off-the-shelf API has way less friction, if the results are good enough why bother?
I think its a combination of being new to classification models and not using in anger.
LLM Decision models are still relatively new and suffer from a number of issues. Order of choices impacts probability, it starts to break down over 20 classifications and even how you word things impacts probability.
I don’t have a deep understanding of this topic, but wouldn’t you also need a good amount of relevant data to train on?
and because they start off generalized, you can train them against multiple of your workloads (they'll start with some generalized performance/world knowledge about them)
Which allows you to embed proprietary processes/data (world knowledge only you may have) across multiple domains into a model cheaply
>These 200 test lesions, and every other photograph of the same lesion or patient, were removed from its data (including the validation set) before training, so it had never seen them, and they were not used to choose the model or set its threshold.
This is just benchmaxxing. A benchmaxxed model will always beat an unbenchmaxxed model, but it will be brittle.
You can always beat a specific dataset with a small model by finetuning on a train/test split. But it doesn't generalize well to 'wild' images, because they are out-of-domain.
I believe the normal term is "gaming a benchmark test".
Nothing in what you quoted is related to that. It's a normal way to train and test a classifier.
The only way to determine if it's brittle is to test on a different dataset and look at the sensitivity and specificity.
Even so, there is nothing wrong with a model that is trained to a narrow domain when it doesn't need to deal with "wild" images.
I remember being blown away by CNNs and GANs when they first gained popularity. I do think, with skin cancer diagnosis, a transformer architecture is going to be the better bet.
Slide rules displaced tables, calculators displaced slide rules and calculators themselves were displaced for the most part (they are still in use, in education but not nearly as much as they used to be outside of that) by the PC.
LLMs do that sort of thing for just about every information processing aid.
The harness in my experience makes a huge different too. Out of curiosity I tried having Gemini chat (with Argon) try to solve a level of this new puzzle game I'm linking (Order of the Sinking Star). It just got in an infinite loop and crashed. Then I try Antigravity with Flash 3.8 and it solved it easily. I saw the harness looked at other sokoban puzzles, coded some stuff, tried to replicate the level, etc. All things that a pure Gemini Chat doesn't really do.
LLMs are great for the high level harness, but if you want to count letters for example, it will still rely on "tools" like a piece of code to do it more reliably. Similarly in the above example, you wouldn't have the LLM directly try to analyze the images, but instead fetch and use the best tool for the job. But that can only be as good as the harness/tools it has access to.
Doesn't matter the domain or the task, the answer from some researchers is "LLM" always.
We're messaging each other right now inside a heavily sandboxed virtual environment that interprets XML-like markup and runs a text-based JITted scripting language from the 90s. It doesn't really matter that it's extremely inefficient, since it's still effectively instant.
Effectively instant means nothing, since everything is fast for small n. I love to give this example from a codebase I have worked on during my Ph.D. The problem which took half an hour under Matlab was done in ~40 seconds with mine, however, a thousand-step version of the same problem would take 11 hours. Saving a single second from that 40 second loop would shave my work by ~20 minutes, and this will compound for bigger problems still.
Just because we see the results instantly, doesn't mean it's fast or efficient by any means. It means we might forego optimizing it for sufficiently small n, if we don't care about that part of the code. That's all.
The app is less efficient only in an abstract, impractical way.
It depends on the calculator and task at hand. Consider graphing, integration or any programmable needs, and they might converge. ALU of a processor is pretty efficient at what it does.
> The app is less efficient only in an abstract, impractical way.
Again, depends, as I said above. You tend to brush efficiency away because you only mentally measure time to completion, only, which is understandable.
I'm surprised he's surprised.
> A small, old, specialised network using an outdated architecture recognises skin lesions far better than two frontier LLMs.
Yes of course. A 30m params Katago network with one playout is superhuman in playing go, while frontier LLMs have over 10,000 times more parameters and aren't any good at playing go. They're good at other things (such as helping you create a network that's good at playing go).
However, I think it kinda misses the potential of llms for diagnosis. What happens if we also include journal notes? What if we let the model generate questions to ask the patient and include those? What if the images aren't perfect examples and happen to contain out of distribution content like a tattoo?
The problem is the narrow models are always super fragile. You change the lighting, zoom level, skin tone, or give it a picture of a cat (or more dangerously a misaligned image) instead of a skin lesion, and it breaks completely.
None of this is new, it’s why narrow classifiers peaked almost 10 years ago and why large general transformer models took over. If you have a task where the data distribution is always identical and has no long tail or outliers then inception or resnet or whatever is great. Problem is these are vanishingly rare.
That being said, I'm sure a frontier LLM could make some improvements to your CNN.
Llms have their applications. Other models still do.
This is like saying I compared this against photos for sure stored in a home swimming pool (CNN), compared to photos presumably stored in an ocean (generalized LLM).
I use LLMs where I can, but I'm no automatic fan of LLMs, a healthier comparison here seems to be what if a general purpose LLM was provided those CNN images, and perhaps what does a fine tuned local model with the CNN images perform like?
It still might not be better, but I would suspect it's closer, and a more realistic apples to apples comparison.
General assistant models compared to task specific models seem to be a well known spot where things don't always fit, but might be improving.
Which LLM? GPT-6 Astra and Claude Fable 5.1, not specified exactly which version
Obviously this is a bit like comparing an 100 iq radiologist, 20 years experience, with a just-tested-at-iq-130 6 year old and observing the radiologist beat the crap out of the kid. Was anybody really expecting a different outcome? Let's see after fine tuning.
Btw: the actual lesson from that network when it came out, which is really important is that Inception v6, the Google trained version (ie. can recognize cats, halloween masks and airplanes) beat, with a wide margin, the version that was trained from scratch on clinical images (and still didn't do so well, 62%. Not even really good enough for a first triage).
So yes, this is an important finding that LLMs are not better than experience.
It's comparing a fine-tuned CNN to general purpose models on a tiny dataset.
I don't think we should need to be reminded that frontier models aren't particularly good at most tasks. Just like we shouldn't need to be reminded that a hammer is not particularly good for driving a screw, or writing an essay.
It's an admission of magical thinking about a tool that's made out of the same material of every other digital tool since the invention of computing.
I will not flag it because the post is fine, it's the voters who I believe are acting poorly.