2026-03-09 00:00:00:0何思琦3014413410http://paper.people.com.cn/rmrb/pc/content/202603/09/content_30144134.htmlhttp://paper.people.com.cn/rmrb/pad/content/202603/09/content_30144134.html11921 东方烟火跨越山海（两会笔记）
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В ЕС призвали расширить антироссийские санкции на третьи страныДепутат ЕП Луэна призвал ужесточить санкции за экспорт через третьи страны в РФ。业内人士推荐新收录的资料作为进阶阅读
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.，更多细节参见新收录的资料