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You can train idea-to-paper models on tons of papers with code. There are many examples of paper impl on github.


yes - LLMs tuned based on data science publications will be great. need a dataset of papers with reliable and well-performed analysis. Notably though it works quite well even with the general purpose LLMs. The key was to break the complex process into smaller steps where results from upstream steps are used downstream. that also creates papers where every downstream result is programmatically linked to upstream data.




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