most citedMenatQA: A New Dataset for Testing the Temporal Comprehension and Reasoning Abilities of Large Language Models

4 citations · 6 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2024

Towards Effective, Efficient and Unsupervised Social Event Detection in the Hyperbolic Space

Xiaoyan Yu, Yifan Wei, Shuaishuai Zhou +5

The vast, complex, and dynamic nature of social message data has posed challenges to social event detection (SED). Despite considerable effort, these challenges persist, often resu…

cs.CL20241 cited

Multi-View Incongruity Learning for Multimodal Sarcasm Detection

Diandian Guo, Cong Cao, Fangfang Yuan +5

Multimodal sarcasm detection (MSD) is essential for various downstream tasks. Existing MSD methods tend to rely on spurious correlations. These methods often mistakenly prioritize…

cs.CV2024

Arctique: An artificial histopathological dataset unifying realism and controllability for uncertainty quantification

Jannik Franzen, Claudia Winklmayr, Vanessa E. Guarino +6

Uncertainty Quantification (UQ) is crucial for reliable image segmentation. Yet, while the field sees continual development of novel methods, a lack of agreed-upon benchmarks limit…

cs.CL2024

UNO Arena for Evaluating Sequential Decision-Making Capability of Large Language Models

Zhanyue Qin, Haochuan Wang, Deyuan Liu +9

Sequential decision-making refers to algorithms that take into account the dynamics of the environment, where early decisions affect subsequent decisions. With large language model…

physics.soc-ph20241 cited

Locating influential nodes in hypergraphs via fuzzy collective influence

Su-Su Zhang, Xiaoyan Yu, Gui-Quan Sun +2

Complex contagion phenomena, such as the spread of information or contagious diseases, often occur among the population due to higher-order interactions between individuals. Indivi…

cs.CL20234 cited

MenatQA: A New Dataset for Testing the Temporal Comprehension and Reasoning Abilities of Large Language Models

Yifan Wei, Yisong Su, Huanhuan Ma +5

Large language models (LLMs) have shown nearly saturated performance on many natural language processing (NLP) tasks. As a result, it is natural for people to believe that LLMs hav…