72 citations · 133 across the 8 of their papers we have counts for
8 papers
A robust association between LLM use and scientific productivity: Assessing stopping-time selection
Keigo Kusumegi, Xinyu Yang, Paul Ginsparg +3
Renault, Bergeaud, and Bosquet (hereafter RBB) argue that dating LLM adoption as the first month in which an author's abstract is flagged induces a stopping-time selection that can…
Live Interactive Training for Video Segmentation
Xinyu Yang, Haozheng Yu, Yihong Sun +2
Interactive video segmentation often requires many user interventions for robust performance in challenging scenarios (e.g., occlusions, object separations, camouflage, etc.). Yet,…
Scientific production in the era of Large Language Models
Keigo Kusumegi, Xinyu Yang, Paul Ginsparg +3
Large Language Models (LLMs) are rapidly reshaping scientific research. We analyze these changes in multiple, large-scale datasets with 2.1M preprints, 28K peer review reports, and…
Tracking and Understanding Object Transformations
Yihong Sun, Xinyu Yang, Jennifer J. Sun +1
Real-world objects frequently undergo state transformations. From an apple being cut into pieces to a butterfly emerging from its cocoon, tracking through these changes is importan…
What's documented in AI? Systematic Analysis of 32K AI Model Cards
Weixin Liang, Nazneen Rajani, Xinyu Yang +5
The rapid proliferation of AI models has underscored the importance of thorough documentation, as it enables users to understand, trust, and effectively utilize these models in var…
Navigating Dataset Documentations in AI: A Large-Scale Analysis of Dataset Cards on Hugging Face
Xinyu Yang, Weixin Liang, James Zou
Advances in machine learning are closely tied to the creation of datasets. While data documentation is widely recognized as essential to the reliability, reproducibility, and trans…
Can large language models provide useful feedback on research papers? A large-scale empirical analysis
Weixin Liang, Yuhui Zhang, Hancheng Cao +9
Expert feedback lays the foundation of rigorous research. However, the rapid growth of scholarly production and intricate knowledge specialization challenge the conventional scient…
Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations
Weixin Liang, Yining Mao, Yongchan Kwon +2
Understanding the performance of machine learning (ML) models across diverse data distributions is critically important for reliable applications. Despite recent empirical studies…