ResearchAgent: Iterative Research Idea Generation Using LLMs
1–10 of 66 posts
Re: ResearchAgent: Iterative Research Idea Generation Using LLMs
#2Cool 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.
Re: ResearchAgent: Iterative Research Idea Generation Using LLMs
#3Cool 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.
I think that ship has sailed, if you believe the paper (which I do).
LLMs are already super-human at some highly abstract creative tasks, including research.
There are numerous examples of LLMs solving problems that couldn't be found in the training data. They can also be improved by using reasoning methods like truth tables or causal language. See Orca from Microsoft for example.
Re: ResearchAgent: Iterative Research Idea Generation Using LLMs
#4Cool 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 just need to be better at it than humans, which is a rather low bar when you go beyond two unrelated fields.
Re: ResearchAgent: Iterative Research Idea Generation Using LLMs
#5Cool 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.
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 recognizing simple character patterns, like putting a consonant after a vowel. Then it learns syllables, and then words, and then sentences. With enough training (and data and parameters, of course) you eventually yield a model like GPT-4 that can write better code than many programmers.
It’s not always that clear cut, but you can clearly observe it moving up the chain of abstraction as the training loss decreases.
What happens when you go even bigger than GPT-4? We have every reason to believe that the models will be able to think more abstractly.
Your “never gonna work” comment flies in the face of exponential curve we find ourselves on.
Re: ResearchAgent: Iterative Research Idea Generation Using LLMs
#6Cool 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.
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Re: ResearchAgent: Iterative Research Idea Generation Using LLMs
#7Cool 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.
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.
Re: ResearchAgent: Iterative Research Idea Generation Using LLMs
#8I'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.
Re: ResearchAgent: Iterative Research Idea Generation Using LLMs
#9Cool 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.
I have asked chat GPT to generate hypotheses on my PhD topic that I know every single piece of existing literature about and it actually threw out some very interesting ideas that do not exist out there yet (this was before they lobotomized it).
Re: ResearchAgent: Iterative Research Idea Generation Using LLMs
#10I'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?