tionally, for the questions in test set whose responses have
ground-truth human preference, e.g., the number of likes,
we develop the ranking questions that always rank five can-
didates. For evaluation, we adopt the top-1 accuracy and
the widely used ranking metric,i.e., Normalized Discounted
Cumulative Gain (NDCG) [81, 82]. We provide more ex-
perimental details in the Appendix.
5.2. Evaluation by Choice and Ranking Questions
Evaluation on Multimodal Multilingual LLMs. We
plug our associable instruction tuning (AIT) and our CLoT
into the SoTA open-source multimodal multilingual model
Qwen-VL [1] to obtain Qwen-VL+AIT and Qwen-VL+CLoT,
respectively. Table 2 shows that, on three tasks (IT2T, I2T
and T2T) which include English, Chinese and Japanese
questions, Qwen-VL achieves the best LoT performance
among all baselines in most cases. In comparison, Qwen-
VL+AIT achieves a noticeable improvement on the SoTA
Qwen with average accuracy enhancements of 6.8%, 6.7%,
Let’sThinkOutsidetheBox