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ResearchAgent: Iterative Research Idea Generation Using LLMs

arxiv.org

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Re: ResearchAgent: Iterative Research Idea Generation Using LLMs

#11

Post by esafak »

ethanwillis wrote: post #7
bigyikes wrote: post #5

Earlier quoted context omitted.

Ignoring the “spits out training data” bit which is at best misleading, it’s interesting that you use the word “abstract” here. I recently followed Karpathy’s GPT-from-scratch tutorial and was fascinated with how clearly you could see the models improving. With no training, the model spits out uniformly random text. With a bit of training, the model starts generating gibberish. With further training, the model starts…

If we keep extrapolating eventually GPT will be omniscient. I really can't think of any reason why that wouldn't be the case, given the exponential curve we find ourselves on.

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?

Re: ResearchAgent: Iterative Research Idea Generation Using LLMs

#12

Post by [deleted] »

esafak wrote: post #11
ethanwillis wrote: post #7

Earlier quoted context omitted.

If we keep extrapolating eventually GPT will be omniscient. I really can't think of any reason why that wouldn't be the case, given the exponential curve we find ourselves on.

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?

[deleted]

Re: ResearchAgent: Iterative Research Idea Generation Using LLMs

#13

Post by CuriouslyC »

not-chatgpt wrote: post #2

Cool idea. Never gonna work. LLMs are still generative models that spits out training data, incapable of highly abstract creative tasks like research. I still remember all the GPT-2 based startup idea generators that spits out pseudo-feasible startups.

they don't just spit out training data, they generalize from training data. They can look at an existing situation and suggest lines of experimentation or analysis that might lead to interesting results based on similar contexts in other sciences or previous research. They're undertrained on bleeding edge science so they're going to falter there but they can apply methodology just fine.

Re: ResearchAgent: Iterative Research Idea Generation Using LLMs

#15

Post by dragonwriter »

esafak wrote: post #11
ethanwillis wrote: post #7

Earlier quoted context omitted.

If we keep extrapolating eventually GPT will be omniscient. I really can't think of any reason why that wouldn't be the case, given the exponential curve we find ourselves on.

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?

With real world phenomena that have resource constraints anywhere, a good rule of thumb is: if it looks like an exponential curve, walks like an exponential curve, and quacks like an exponential curve, it’s definitely a logistic curve

Re: ResearchAgent: Iterative Research Idea Generation Using LLMs

#16

Post by robwwilliams »

pedalpete wrote: post #8

I've found where LLMs can be useful in this context is around free-associations. Because they don't really "know" about things, they regularly grasp at straws or misconstrue intended meaning. This, along with the volume of language (let's not call it knowledge) result in the LLMs occasionally bringing in a new element which can be useful.

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.

Re: ResearchAgent: Iterative Research Idea Generation Using LLMs

#17

Post by bongodongobob »

gotts wrote: post #10
pedalpete wrote: post #8

I've found where LLMs can be useful in this context is around free-associations. Because they don't really "know" about things, they regularly grasp at straws or misconstrue intended meaning. This, along with the volume of language (let's not call it knowledge) result in the LLMs occasionally bringing in a new element which can be useful.

Can you list some examples where free-associations from LLM were useful to you?

Assume free-associations = hallucinations. Assume hallucinations are exactly what makes LLMs useful and your question can be rephrased as "Can you list some examples where LLMs were useful to you?"

Re: ResearchAgent: Iterative Research Idea Generation Using LLMs

#18

Post by KhoomeiK »

A group of PhD students at Stanford recently wanted to take AI/ML research ideas generated by LLMs like this and have teams of engineers execute on them at a hackathon. We were getting things prepared at AGI House SF to host the hackathon with them when we learned that the study did not pass ethical review.

I think automating science is an important research direction nonetheless.

Re: ResearchAgent: Iterative Research Idea Generation Using LLMs

#19

Post by inference-lord »

ethanwillis wrote: post #7
bigyikes wrote: post #5

Earlier quoted context omitted.

Ignoring the “spits out training data” bit which is at best misleading, it’s interesting that you use the word “abstract” here. I recently followed Karpathy’s GPT-from-scratch tutorial and was fascinated with how clearly you could see the models improving. With no training, the model spits out uniformly random text. With a bit of training, the model starts generating gibberish. With further training, the model starts…

If we keep extrapolating eventually GPT will be omniscient. I really can't think of any reason why that wouldn't be the case, given the exponential curve we find ourselves on.

I think they're being factitious?

Re: ResearchAgent: Iterative Research Idea Generation Using LLMs

#20

Post by srcreigh »

KhoomeiK wrote: post #18

A group of PhD students at Stanford recently wanted to take AI/ML research ideas generated by LLMs like this and have teams of engineers execute on them at a hackathon. We were getting things prepared at AGI House SF to host the hackathon with them when we learned that the study did not pass ethical review . I think automating science is an important research direction nonetheless.

That’s pretty wild. What was the reason behind failing ethics review?
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