most citedPosition: AI Evaluation Should Learn from How We Test Humans

15 citations · 19 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2024★ 1 cited

Pandora: Towards General World Model with Natural Language Actions and Video States

Jiannan Xiang, Guangyi Liu, Yi Gu +10

World models simulate future states of the world in response to different actions. They facilitate interactive content creation and provides a foundation for grounded, long-horizon…

cs.CL2024

EduNLP: Towards a Unified and Modularized Library for Educational Resources

Zhenya Huang, Yuting Ning, Longhu Qin +8

Educational resource understanding is vital to online learning platforms, which have demonstrated growing applications recently. However, researchers and developers always struggle…

cs.CL2023★ 15 cited

Position: AI Evaluation Should Learn from How We Test Humans

Yan Zhuang, Qi Liu, Zachary A. Pardos +5

As AI systems continue to evolve, their rigorous evaluation becomes crucial for their development and deployment. Researchers have constructed various large-scale benchmarks to det…

cs.CL2023★ 3 cited

A Novel Approach for Auto-Formulation of Optimization Problems

Yuting Ning, Jiayu Liu, Longhu Qin +6

In the Natural Language for Optimization (NL4Opt) NeurIPS 2022 competition, competitors focus on improving the accessibility and usability of optimization solvers, with the aim of…

cs.CL2023

Towards a Holistic Understanding of Mathematical Questions with Contrastive Pre-training

Yuting Ning, Zhenya Huang, Xin Lin +4

Understanding mathematical questions effectively is a crucial task, which can benefit many applications, such as difficulty estimation. Researchers have drawn much attention to des…