6 citations · 9 across the 6 of their papers we have counts for
6 papers
From Critic to Confidence: PPO for Language-Based Quantitative Prediction with Confidence Estimation
Mehak Dhaliwal, Rasta Tadayon, Andong Hua +2
LLMs can perform language-based quantitative prediction from unstructured inputs, but remain susceptible to hallucinations and overconfident errors, making it critical to know not…
Mix from Failure: Confusion-Pairing Mixup for Long-Tailed Recognition
Youngseok Yoon, Sangwoo Hong, Hyungjun Joo +3
Long-tailed image recognition is a computer vision problem considering a real-world class distribution rather than an artificial uniform. Existing methods typically detour the prob…
LLMs are Biased Teachers: Evaluating LLM Bias in Personalized Education
Iain Weissburg, Sathvika Anand, Sharon Levy +1
With the increasing adoption of large language models (LLMs) in education, concerns about inherent biases in these models have gained prominence. We evaluate LLMs for bias in the p…
Model Collapse in the Self-Consuming Chain of Diffusion Finetuning: A Novel Perspective from Quantitative Trait Modeling
Youngseok Yoon, Dainong Hu, Iain Weissburg +2
Model collapse, the severe degradation of generative models when iteratively trained on their own outputs, has gained significant attention in recent years. This paper examines Cha…
A Survey on Data Selection for Language Models
Alon Albalak, Yanai Elazar, Sang Michael Xie +11
A major factor in the recent success of large language models is the use of enormous and ever-growing text datasets for unsupervised pre-training. However, naively training a model…
Coded Computing for Fault-Tolerant Parallel QR Decomposition
Quang Minh Nguyen, Iain Weissburg, Haewon Jeong
QR decomposition is an essential operation for solving linear equations and obtaining least-squares solutions. In high-performance computing systems, large-scale parallel QR decomp…