"Soon, we will look back on this era where we are using open GNSS signals and think, 'God, we were mad, that was really not a smart move'," he says.
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.，详情可参考新收录的资料
，推荐阅读新收录的资料获取更多信息
import { Result, ok, err, safeTry } from '@/apx/Error/result';。新收录的资料是该领域的重要参考
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