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.
1987�N�ɃI�[�X�g���A�œo�ꂵ���A�G�i�W�[�h�����N��\���������i�Ƃ��đn���������̂����u���b�h�u�� �G�i�W�[�h�����N�v�ł��BAmazon.co.jp�ł́A250ml�ʂ�24�{�Z�b�g�������i��1�{�����150�~���炢�̐����Ȑ��i�ŁA�܂Ƃߔ����Ă��܂��B
，更多细节参见搜狗输入法
Автор материала: Нина Ташевская (Специалист рубрики "Жизненное пространство")
Don Lokke created this "Wally" illustration—a tuxedo-clad walrus—using the high-resolution BIG format.