Finding patterns in large datasets is one of the things LLMs are really good at. Genetics is an area where scientists have already done impressive things with LLMs.
However you feel about LLMs, and I say this because you don't have to use them for very long before you witness how useful they can be for large datasets so I'm guessing you're not a fan, they are undeniably incredible tools in some areas of science.
https://news.stanford.edu/stories/2025/02/generative-ai-tool...
In reference to the second article: who cares? What we care about is experimental verification. I could see maybe accurate prediction being helpful in focusing funding, but you still gotta do the experimentation.
Not disagreeing with your initial statement about LLMs being good and finding patterns in datasets btw.
This is also true of lots of human research, there's always a theory side of research that guides the experimental side. Even if just informal, experimental researchers have priors for what experimental verification they should attempt.
Yeah, there’s an infinite numbers of experiments you could run but obviously infinite resources don’t exist, so you need theory to guide where to look. For example, computational methods in bioinformatics to guess a protein function so that experimental researchers can verify the protein function (which takes weeks to months for a given protein function hypothesis) is an entire field.
You need to search in both likely and unlikely places. This is pretty common in high dimensional search spaces. Searching only in the most likely places gets you stuck in local minima
As a scientist, the two links you provided are severely lacking in utility.
The first developed a model to calculate protein function based on DNA sequence - yet provides no results of testing of the model. Until it does, it’s no better than the hundreds of predictive models thrown on the trash heap of science.
The second tested a models “ability to predict neuroscience results” (which reads really oddly). How did they test it? Pitted humans against LLMs in determining which published abstracts were correct.
Well yeah? That’s exactly what LLMs are good at - predicting language. But science is not advanced by predicting which abstracts of known science are correct.
It reminds me of my days in working with computational chemists - we had an x-ray structure of the molecule bound to the target. You can’t get much better than that at hard, objective data.
“Oh yeah, if you just add a methyl group here you’ll improve binding by an order of magnitude”.
So we went back to the lab, spent a week synthesizing the molecule, sent it to the biologists for a binding study. And the new molecule was 50% worse at binding.
And that’s not to blame the computation chemist. Biology is really damn hard. Scientists are constantly being surprised at results that are contradictory to current knowledge.
Could LLMs be used in the future to help come up with broad hypotheses in new areas? Sure! Are the hypotheses going to prove fruitless most of the time? Yes! But that’s science.
But any claim of a massive leap in scientific productivity (whether LLMs or something else) should be taken with a grain of salt.
> Finding patterns in large datasets is one of the things LLMs are really good at.
Where by "good at" you mean "are totally shit at"?
They routinely hallucinate things even on tiny datasets like codebases.
I don't follow the logic that "it hallucinates so it's useless". In the context of codebases I know for sure that they can be useful. Large datasets too. Are they also really bad at some aspects of dealing with both? Absolutely. Dangerously, humorously bad sometimes.
But the latter doesn't invalidate the former.
> I don't follow the logic that "it hallucinates so it's useless".
I... don't even know how to respond to that.
Also. I didn't say they were useless. Please re-read the claim I responded to.
> Are they also really bad at some aspects of dealing with both? Absolutely. Dangerously, humorously bad sometimes.
Indeed.
Now combine "Finding patterns in large datasets is one of the things LLMs are really good at." with "they hallucinate even on small datasets" and "Are they also really bad at some aspects of dealing with both? Absolutely. Dangerously, humorously bad sometimes"
Translation, in case logic somehow eludes you: if an LLM finds a pattern in a large dataset given that it often hallucinates, dangerously, humorously bad, what are the chances that the pattern it found isn't a hallucination (often subtle one)?
Especially given the undeniable verifiable fact that LLMs are shit at working with large datasets (unless they are explicitly trained on them, but then it still doesn't remove the problem of hallucinations)