deki

deki

AI & ML interests

Object detection, LLMs

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reacted to m-ric's post with πŸš€ 22 minutes ago
We now have a Deep Research for academia: SurveyX automatically writes academic surveys nearly indistinguishable from human-written ones πŸ”₯ Researchers from Beijing and Shanghai just published the first application of a deep research system to academia: their algorithm, given a question, can give you a survey of all papers on the subject. To make a research survey, you generally follow two steps, preparation (collect and organize papers) and writing (outline creation, writing, polishing). Researchers followed the same two steps and automated them. 🎯 For the preparation part, a key part is find all the important references on the given subject. Researchers first cast a wide net of all relevant papers. But then finding the really important ones is like distilling knowledge from a haystack of information. To solve this challenge, they built an β€œAttributeTree” object that structures key information from citations. Ablating these AttributeTrees significantly decreased structure and synthesis scores, so they were really useful! πŸ“ For the writing part, key was to get a synthesis that's both short and true. This is not easy to get with LLMs! So they used methods like LLM-based deduplication to shorten the too verbose listings made by LLMs, and RAG to grab original quotes instead of made-up ones. As a result, their system outperforms previous approaches by far! As assessed by LLM-judges, the quality score os SurveyX even approaches this of human experts, with 4.59/5 vs 4.75/5 πŸ† I advise you to read the paper, it's a great overview of the kind of assistants that we'll get in the short future! πŸ‘‰ https://huggingface.co/papers/2502.14776 Their website shows examples of generated surveys πŸ‘‰ http://www.surveyx.cn/
liked a dataset 11 days ago
open-r1/OpenR1-Math-220k
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