We extracted additional value from existing datasets through reformatting, diversification, and using images as seeds for new data generation. We generated detailed image descriptions alongside original QA pairs for math and science data, had data perform “double-duty” by embedding instruction-following requirements directly into domain-specific QA, created “scrambled,” “caption-matching,” and “what’s changed?” records to improve multi-image reasoning and sequential navigation for CUA scenarios, and diversifying prompt styles to encourage robustness beyond perfectly structured questions.
人 民 网 版 权 所 有 ，未 经 书 面 授 权 禁 止 使 用，详情可参考whatsapp
lululemon 携手刘易斯·汉密尔顿发布「感受练」主题，迎接 F1 中国大奖赛，更多细节参见谷歌
СюжетВзрывы в Краснодаре