4 citations · 6 across the 4 of their papers we have counts for
4 papers · 1 filter
Whose Guardrail? User Identity Shapes the Effectiveness of Model Disclaimers in Mitigating LLM Political Persuasion
Jillian Fisher, Thomas Richardson, Daniel W. Fisher +3
Politically biased AI can shift users' opinions, yet it remains unclear whether model disclaimers protect against such persuasion. We report a preregistered experiment (N=1,586) ex…
Interactive Reasoning: Visualizing and Controlling Chain-of-Thought Reasoning in Large Language Models
Rock Yuren Pang, K. J. Kevin Feng, Shangbin Feng +5
The output quality of large language models (LLMs) can be improved via "reasoning": generating segments of chain-of-thought (CoT) content to further condition the model prior to pr…
Biased AI can Influence Political Decision-Making
Jillian Fisher, Shangbin Feng, Robert Aron +6
As modern large language models (LLMs) become integral to everyday tasks, concerns about their inherent biases and their potential impact on human decision-making have emerged. Whi…
Imagine a dragon made of seaweed: How images enhance learning in Wikipedia
Anita Silva, Maria Tracy, Katharina Reinecke +2
Though images are ubiquitous across Wikipedia, it is not obvious that the image choices optimally support learning. When well selected, images can enhance learning by dual coding,…