i just read this paper on arxiv and im still trying to wrap my head around the implications so the authors got over 100 nlp researchers to write down novel research ideas and then had them review ideas generated by a large language model (llm) without knowing which ones were human generated and which ones were from the llm and the results are pretty fascinating the llm generated ideas were actually judged to be more novel than the human ones p value < 0.05 but slightly weaker in terms of feasibility i mean whats the point of having a novel idea if its not feasible right but still its pretty cool that the llm can come up with stuff that humans havent thought of before

and the authors are saying that this study highlights some of the open problems in building research agents that can generate novel ideas like the llm was bad at self evaluation it couldnt tell which of its own ideas were good or bad and they also found that the llm generated ideas that were too similar to each other lacking diversity i mean thats not surprising right llms are trained on huge datasets but theyre still just pattern recognition machines they dont really understand the context or the implications of what theyre generating

but heres the thing novelty is hard to judge even for experts i mean how do you even define novelty is it just something that nobody has thought of before or is it something that challenges our current understanding of the world and the authors are proposing a follow up study where they actually have researchers execute these ideas into full projects to see if the novelty and feasibility judgements actually translate into meaningful differences in research outcomes which is a great idea i mean thats the only way we can really know if these llms are useful for accelerating scientific discovery or not

anyway im rambling on now but i just think this is a really interesting area of research and im excited to see where it goes can we really use llms to accelerate scientific discovery and what are the limitations of these models and how can we overcome them etc etc