10 citations · 20 across the 8 of their papers we have counts for
13 papers
CT: Counterfactual Causal Reasoning for Sentiment Shifts in Social-Media Conversation Trees
S M Rafiuddin, Atriya Sen
Sentiment in social-media threads does not only vary across posts; it shifts as users react to claims, corrections, evidence, and hostility within a branching reply tree. We study…
Don't Forget It! Conditional Sparse Autoencoder Clamping Works for Unlearning
Matthew Khoriaty, Andrii Shportko, Gustavo Mercier +1
Recent developments in Large Language Model (LLM) capabilities have brought great potential but also posed new risks. For example, LLMs with knowledge of bioweapons, advanced chemi…
Can You Trust LLM Judgments? Reliability of LLM-as-a-Judge
Kayla Schroeder, Zach Wood-Doughty
Large Language Models (LLMs) have become increasingly powerful and ubiquitous, but their stochastic nature poses challenges to the reliability of their outputs. While deterministic…
Controlling for Unobserved Confounding with Large Language Model Classification of Patient Smoking Status
Samuel Lee, Zach Wood-Doughty
Causal understanding is a fundamental goal of evidence-based medicine. When randomization is impossible, causal inference methods allow the estimation of treatment effects from ret…
Reliability of Topic Modeling
Kayla Schroeder, Zach Wood-Doughty
Topic models allow researchers to extract latent factors from text data and use those variables in downstream statistical analyses. However, these methodologies can vary significan…
Segment Anything Model is a Good Teacher for Local Feature Learning
Jingqian Wu, Rongtao Xu, Zach Wood-Doughty +3
Local feature detection and description play an important role in many computer vision tasks, which are designed to detect and describe keypoints in "any scene" and "any downstream…