3 papers
cs.CL2026
Inference-Time Mitigation of Adversarial Political Bias in Large Language Models
Tejaswi V. Panchagnula, Bruce Coburn, Bryce J. Dietrich +3
As Large Language Models (LLMs) become the mainstay for information retrieval and summarization tasks, ensuring that they are always non-partisan and invulnerable to political bias…
stat.AP2026
Causal Inference with Video Features as Treatments
Kentaro Nakamura, Adam Breuer, Michael H. Crespin +2
We develop the first statistical methodology for causal inference with video features as treatments. Video is the most engaging content modality on the internet. A central causal q…
cs.MM2025
Using AI to Summarize US Presidential Campaign TV Advertisement Videos, 1952-2012
Adam Breuer, Bryce J. Dietrich, Michael H. Crespin +3
This paper introduces the largest and most comprehensive dataset of US presidential campaign television advertisements, available in digital format. The dataset also includes machi…