Neural Information Processing Systems http://nips.cc/

We thank all the reviewers for helpful suggestions. We will incorporate the following analysis into our revision. Firstly, we found 4 typical patterns shared by both, as shown in Figure 1. Attention patterns shared by XLNet and BERT . Rows and columns represent query and key respectively.

Recent language models have shown impressive multilingual performance, even when not explicitly trained for it. Despite this, there are concerns about the quality of their outputs across different languages. In this paper, we show how disparity in the treatment of different languages arises at the tokenization stage, well before a model is even invoked. The same text translated into different languages can have drastically different tok-enization lengths, with differences up to 15 times in some cases. These disparities persist even for tokenizers that are intentionally trained for multilingual support.

For details on M1-M5, please refer to Appendix B.3.

Furthermore, it exceeds GroundingDINO-L by 11.0 AP

We show our framework's flexibility by providing a comprehensive

Our theoretical analysis reveals that in this setting, in-context learning is more about identifying the task than about learning it, a result which is in line with a series of recent empirical findings.