Multilingual Alignment is an effective and representative paradigm to enhance LLMs' multilingual capabilities, which transfers the capabilities from the high-resource languages to the low-resource languages. Meanwhile, some research on language-specific neurons provides a new perspective to analyze and understand LLMs' mechanisms. However, we find that there are many neurons that are shared by multiple but not all languages and cannot be correctly classified. In this work, we propose a ternary classification methodology that categorizes neurons into three types, including language-specific neurons, language-related neurons, and general neurons. And we propose a corresponding identification algorithm to distinguish these different types of neurons. Furthermore, based on the distributional characteristics of different types of neurons, we divide the LLMs' internal process for multilingual inference into four parts: (1) multilingual understanding, (2) shared semantic space reasoning, (3) multilingual output space transformation, and (4) vocabulary space outputting. Additionally, we systematically analyze the models before and after alignment with a focus on different types of neurons. We also analyze the phenomenon of ''Spontaneous Multilingual Alignment''. Overall, our work conducts a comprehensive investigation based on different types of neurons, providing empirical results and valuable insights to better understand multilingual alignment and multilingual capabilities of LLMs.

Facial depression recognition is one of the current hot topics. Mainstream methods mainly focus on how to design deep models to effectively extract the difference in facial movements between depressed patients and healthy people. However, this difference changes when the stimulus source to which the subjects are exposed changes. This leads to the performance degradation in cross-stimulus situation and limits the practical application of this technology. We hold the opinion that why depressed patients show behavioral characteristics different from healthy people is that they have a specific stable pattern of responding to stimulus. Therefore, we incorporate stimuli into the modeling process for the first time and employ deep networks to learn stable representations between stimulus and response to achieve stable and effective modeling. Specifically, we propose a deep modeling framework to learn the stimulus-response pattern of the subject through the interaction relationship between the stimulus videos and the subject’s facial movements. We constructed a balanced depression dataset of 364 individuals with three different stimulus videos to verify the effectiveness of our method. The results show that our method achieves state-of-the-art and the best generalization performance in depression recognition. This stimulus-response pattern modeling provides a new perspective for recognizing depression.

Shimao Zhang, Changjiang Gao, Wenhao Zhu, Jiajun Chen, Xin Huang, Xue Han, Junlan Feng, Chao Deng, Shujian Huang. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

We investigate the problem of distributed online convex optimization with complicated constraints, in which the projection operation could be the computational bottleneck. To avoid projections, distributed online projection-free methods have been proposed and attain an O(T^{3/4}) regret bound for general convex losses. However, they cannot utilize the smoothness condition, which has been exploited in the centralized setting to improve the regret. In this paper, we propose a new distributed online projection-free method with a tighter regret bound of O(T^{2/3}) for smooth and convex losses. Specifically, we first provide a distributed extension of Follow-the-Perturbed-Leader so that the smoothness can be utilized in the distributed setting. Then, we reduce the computational cost via sampling and blocking techniques. In this way, our method only needs to solve one linear optimization per round on average. Finally, we conduct experiments on benchmark datasets to verify the effectiveness of our proposed method.