The process of improving open-source data began by manually reviewing samples from each dataset. Typically, 5 to 10 minutes were sufficient to classify data as excellent-quality, good questions with wrong answers, low-quality questions or images, or high-quality with formatting errors. Excellent data was kept largely unchanged. For data with incorrect answers or poor-quality captions, we re-generated responses using GPT-4o and o4-mini, excluding datasets where error rates remained too high. Low-quality questions proved difficult to salvage, but when the images themselves were high quality, we repurposed them as seeds for new caption or visual question answering (VQA) data. Datasets with fundamentally flawed images were excluded entirely. We also fixed a surprisingly large number of formatting and logical errors across widely used open-source datasets.
If you're a developer, Google's technical documentation offers much more detail. For everyone else, keep an eye out for those Play Store labels and consider steering clear of those apps until their devs clean things up.
，更多细节参见PDF资料
Polyunsaturated lipid senolytics exploit a ferroptotic vulnerability in senescent cells。业内人士推荐新收录的资料作为进阶阅读
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