6 papers · 1 filter
FrontierCS: Evolving Challenges for Evolving Intelligence
Qiuyang Mang, Wenhao Chai, Zhifei Li +48
We introduce FrontierCS, a benchmark of 156 open-ended problems across diverse areas of computer science, designed and reviewed by experts, including CS PhDs and top-tier competiti…
Robust Uncertainty Quantification for Self-Evolving Large Language Models via Continual Domain Pretraining
Xiaofan Zhou, Lu Cheng
Continual Learning (CL) is essential for enabling self-evolving large language models (LLMs) to adapt and remain effective amid rapid knowledge growth. Yet, despite its importance,…
Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges
Usman Gohar, Zeyu Tang, Jialu Wang +4
The widespread integration of Machine Learning systems in daily life, particularly in high-stakes domains, has raised concerns about the fairness implications. While prior works ha…
Threshold Filtering Packing for Supervised Fine-Tuning: Training Related Samples within Packs
Jiancheng Dong, Lei Jiang, Wei Jin +1
Packing for Supervised Fine-Tuning (SFT) in autoregressive models involves concatenating data points of varying lengths until reaching the designed maximum length to facilitate GPU…
Evaluating LLMs Capabilities Towards Understanding Social Dynamics
Anique Tahir, Lu Cheng, Manuel Sandoval +3
Social media discourse involves people from different backgrounds, beliefs, and motives. Thus, often such discourse can devolve into toxic interactions. Generative Models, such as…
Different Horses for Different Courses: Comparing Bias Mitigation Algorithms in ML
Prakhar Ganesh, Usman Gohar, Lu Cheng +1
With fairness concerns gaining significant attention in Machine Learning (ML), several bias mitigation techniques have been proposed, often compared against each other to find the…