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ResearchAgent: Iterative Research Idea Generation Using LLMs

arxiv.org

61–66 of 66 posts

Re: ResearchAgent: Iterative Research Idea Generation Using LLMs

#61
post #11

Earlier quoted context omitted.

How do you know you're not on a logistic curve? Don't you think costs and the availability of training data might impose some constraints?

The entire universe is training data.

It is, but we -- humans, and computers -- are limited in our ability to learn from it. We both learn more easily from structured data, like textbooks.

Re: ResearchAgent: Iterative Research Idea Generation Using LLMs

#63
post #42

The ideas aren't the hard part.

This. Any researcher should, over a lunch, be able to generate more idea than can be tackled in a life time.

The number of the ideas has nothing to do with the quality of the ideas. Some ideas a gold, many aren’t.

Re: ResearchAgent: Iterative Research Idea Generation Using LLMs

#65

Earlier quoted context omitted.

rsfern is asking exactly that

No, I'm asking for an example of an idea that an LLM might produce that is too dangerous to implement but nobody at the hackathon has the relevant experience to notice. You can shut down any endeavour by imagining boogeymen that aren't actually real.

I don’t think anything needs to be shut down necessarily, I’m just suggesting reasons why a reasonable ethics board might be hesitant to green light such a hackathon if it’s not clear the organizers have done their due diligence on safety

I might be biased in terms of the safety profile, my background is materials and chemistry, and there are loads of ways you can get into trouble if you don’t really have experience in the materials and synthesis routes you’re working with

One example I’ve heard of from my field (alloy design) is an ML model that suggested an composition high in Magnesium - perfectly reasonable if you’re interested in lightweight strong alloys, but the synthesis method was arc melting, which is a high risk for starting a metal fire if you aren’t careful because Mg has a low vapor pressure

If you’re doing organic chemistry it’s maybe even worse because there can be all kinds of side products or runaway exothermic reactions, and if you’re doing novel chemistry it might take deep experience in the field to know of those things are likely

All these concerns are manageable, but I think an ethics review panel would want to at least see that there is a reasonable safety review process in place before letting students try out random experiments in topic areas in which the models likely haven’t been fine tuned with safety in mind.

Re: ResearchAgent: Iterative Research Idea Generation Using LLMs

#66
post #43

Earlier quoted context omitted.

This approach is already useful in functional genomics. A common type of question requires analysis of hundreds of potentially functional sequence variants. Hybrid LLM+ approaches are beginning to improve efficiency of ranking candidates and even proposing tests and soon I hope—higher order non-linear interactions among DNA variants.

I am interested in this. Can you point to a reference about the application of LLMs to sequence secreening? Thanks.

Scaling if context window size has been a problem but now good potential of solutions using mamba.

HyenaDNA is one to look at wrt DNA.

And here are some other interesting links from Erik Garrison—a leader in pangenomics.

https://hazyresearch.stanford.edu/blog/2023-06-29-hyena-dna

https://github.com/instadeepai/nucleotide-transformer

https://dl.acm.org/doi/pdf/10.1145/3535508.3545512

https://github.com/dnbaker/bioseq

https://huggingface.co/AIRI-Institute/gena-lm-bert-base

https://discuss.huggingface.co/t/dna-long-sequence-tokenizat...

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