6 papers
Inducing Artificial Uncertainty in Language Models
Sophia Hager, Simon Zeng, Nicholas Andrews
In safety-critical applications, language models should be able to characterize their uncertainty with meaningful probabilities. Many uncertainty quantification approaches require…
Does Local News Stay Local?: Online Content Shifts in Sinclair-Acquired Stations
Miriam Wanner, Sophia Hager, Anjalie Field
Local news stations are often considered to be reliable sources of non-politicized information, particularly local concerns that residents care about. Because these stations are tr…
RuleForge: Automated Generation and Validation for Web Vulnerability Detection at Scale
Ayush Garg, Sophia Hager, Jacob Montiel +5
Security teams face a challenge: the volume of newly disclosed Common Vulnerabilities and Exposures (CVEs) far exceeds the capacity to manually develop detection mechanisms. In 202…
Uncertainty Distillation: Teaching Language Models to Express Semantic Confidence
Sophia Hager, David Mueller, Kevin Duh +1
As large language models (LLMs) are increasingly used for factual question-answering, it becomes more important for LLMs to have the capability to communicate the likelihood that t…
Hell or High Water: Evaluating Agentic Recovery from External Failures
Andrew Wang, Sophia Hager, Adi Asija +2
As language model agents are applied to real world problems of increasing complexity, they will be expected to formulate plans across large search spaces. If those plans fail for r…
Learning Extrapolative Sequence Transformations from Markov Chains
Sophia Hager, Aleem Khan, Andrew Wang +1
Most successful applications of deep learning involve similar training and test conditions. However, tasks such as biological sequence design involve searching for sequences that i…