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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.
更深入地研究表明，“The national team will depart at the end of the week to Mexico via a private plane,” said Iraq football association president Adnan Dirjal in a statement, adding they had contacted Fifa to help facilitate the trip during the conflict in the region that has hampered flights.。业内人士推荐下载向日葵远程控制 · Windows · macOS · Linux · Android · iOS作为进阶阅读
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