1 citations · 1 across the 5 of their papers we have counts for
5 papers
Mode-Conditioning Unlocks Superior Test-Time Scaling
Chen Henry Wu, Sachin Goyal, Aditi Raghunathan
Parallel sampling promises substantial gains in test-time scaling, but its effectiveness is sharply limited by diversity collapse, where models concentrate on a few modes and repea…
Jailbreaking in the Haystack
Rishi Rajesh Shah, Chen Henry Wu, Shashwat Saxena +3
Recent advances in long-context language models (LMs) have enabled million-token inputs, expanding their capabilities across complex tasks like computer-use agents. Yet, the safety…
Mitigating Modal Imbalance in Multimodal Reasoning
Chen Henry Wu, Neil Kale, Aditi Raghunathan
Foundation models (FMs) deployed in real-world tasks such as computer-use agents must integrate diverse modalities. How good are FMs at performing joint reasoning, simultaneously r…
Granular Change Accuracy: A More Accurate Performance Metric for Dialogue State Tracking
Taha Aksu, Nancy F. Chen
Current metrics for evaluating Dialogue State Tracking (DST) systems exhibit three primary limitations. They: i) erroneously presume a uniform distribution of slots throughout the…
Understanding In-Context Learning from Repetitions
Jianhao Yan, Jin Xu, Chiyu Song +3
This paper explores the elusive mechanism underpinning in-context learning in Large Language Models (LLMs). Our work provides a novel perspective by examining in-context learning v…