@jeremyphoward
If you're looking for inspiration, I have quite a long list of semi supervised learning tasks for computer vision, many of which have natural analogues in NLP. Here you go ---> https://t.co/dViVrRJnP3
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If you're looking for inspiration, I have quite a long list of semi supervised learning tasks for computer vision, many of which have natural analogues in NLP. Here you go ---> https://t.co/dViVrRJnP3
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"text": "If you're looking for inspiration, I have quite a long list of semi supervised learning tasks for computer vision, many of which have natural analogues in NLP.\n\nHere you go ---> https://t.co/dViVrRJnP3",
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"text": "An aux task for LLM to predict the order of next chunk of tokens. Haha this is so 2018!\n\nIn computer vision this kind of tasks was all the rage for \"self-supervised\" learning! This specific one was called \"jigsaw\". There was also \"relative patch location\" and \"rotation prediction\". All follow the same pattern: you artificially mess with the image, and the model needs to find out what you did.\n\nActually, this was my first paper with @__kolesnikov__ and @XiaohuaZhai , where we basically said: \"wait a minute, all of these aux tasks are kind of a big mess\" (while also setting new sota using them, so we are allowed to say that lol)\n\nIt's interesting because i see more and more previous vision things becoming relevant to language modeling lately, i love it!",
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