most citedDo LLMs Possess a Personality? Making the MBTI Test an Amazing Evaluation for Large Language Models

7 citations · 11 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20241 cited

VideoCoT: A Video Chain-of-Thought Dataset with Active Annotation Tool

Yan Wang, Yawen Zeng, Jingsheng Zheng +3

Multimodal large language models (MLLMs) are flourishing, but mainly focus on images with less attention than videos, especially in sub-fields such as prompt engineering, video cha…

cs.LG20241 cited

Energy-based Automated Model Evaluation

Ru Peng, Heming Zou, Haobo Wang +3

The conventional evaluation protocols on machine learning models rely heavily on a labeled, i.i.d-assumed testing dataset, which is not often present in real world applications. Th…

cs.CV20232 cited

Multi-Prompts Learning with Cross-Modal Alignment for Attribute-based Person Re-Identification

Yajing Zhai, Yawen Zeng, Zhiyong Huang +3

The fine-grained attribute descriptions can significantly supplement the valuable semantic information for person image, which is vital to the success of person re-identification (…

cs.CL20237 cited

Do LLMs Possess a Personality? Making the MBTI Test an Amazing Evaluation for Large Language Models

Keyu Pan, Yawen Zeng

The field of large language models (LLMs) has made significant progress, and their knowledge storage capacity is approaching that of human beings. Furthermore, advanced techniques,…

cs.CL2023

Better Sign Language Translation with Monolingual Data

Ru Peng, Yawen Zeng, Junbo Zhao

Sign language translation (SLT) systems, which are often decomposed into video-to-gloss (V2G) recognition and gloss-to-text (G2T) translation through the pivot gloss, heavily relie…