2 citations · 5 across the 11 of their papers we have counts for
11 papers
Understanding Why Language Models Hallucinate: Testing Reasoning Against Priors
Yangfan Hu, Xuhan Tong, Haoyue Bai +5
Large language models often produce hallucinated answers that violate prompt-level constraints. A key diagnostic question is whether these failures reflect missing knowledge, or wh…
Improving Task Diversity in Label Efficient Supervised Finetuning of LLMs
Abhinav Arabelly, Jagrut Nemade, Robert D Nowak +1
Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, but developing high-performing models for specialized applications often requires sub…
Which LLMs Get the Joke? Probing Non-STEM Reasoning Abilities with HumorBench
Reuben Narad, Siddharth Suresh, Jiayi Chen +5
We present HumorBench, a benchmark designed to evaluate large language models' (LLMs) ability to reason about and explain sophisticated humor in cartoon captions. As reasoning mode…
Deep Active Learning in the Open World
Tian Xie, Jifan Zhang, Haoyue Bai +1
Machine learning models deployed in open-world scenarios often encounter unfamiliar conditions and perform poorly in unanticipated situations. As AI systems advance and find applic…
AHA: Human-Assisted Out-of-Distribution Generalization and Detection
Haoyue Bai, Jifan Zhang, Robert Nowak
Modern machine learning models deployed often encounter distribution shifts in real-world applications, manifesting as covariate or semantic out-of-distribution (OOD) shifts. These…
GPT-4o as the Gold Standard: A Scalable and General Purpose Approach to Filter Language Model Pretraining Data
Jifan Zhang, Ziyue Luo, Jia Liu +2
Large language models require vast amounts of high-quality training data, but effective filtering of web-scale datasets remains a significant challenge. This paper demonstrates tha…
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models
Gantavya Bhatt, Yifang Chen, Arnav M. Das +9
Supervised finetuning (SFT) on instruction datasets has played a crucial role in achieving the remarkable zero-shot generalization capabilities observed in modern large language mo…
Improved Algorithm for Deep Active Learning under Imbalance via Optimal Separation
Shyam Nuggehalli, Jifan Zhang, Lalit Jain +1
Class imbalance severely impacts machine learning performance on minority classes in real-world applications. While various solutions exist, active learning offers a fundamental fi…
LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning
Jifan Zhang, Yifang Chen, Gregory Canal +8
Labeled data are critical to modern machine learning applications, but obtaining labels can be expensive. To mitigate this cost, machine learning methods, such as transfer learning…
Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers
Julian Katz-Samuels, Blake Mason, Kevin Jamieson +1
We consider interactive learning in the realizable setting and develop a general framework to handle problems ranging from best arm identification to active classification. We begi…
Similarity Search for Efficient Active Learning and Search of Rare Concepts
Cody Coleman, Edward Chou, Julian Katz-Samuels +7
Many active learning and search approaches are intractable for large-scale industrial settings with billions of unlabeled examples. Existing approaches search globally for the opti…