Sarah Masud
Sarah Masud is currently a postdoc at the University of Copenhagen, exploring stereotypes and narratives. During her PhD from Indraprastha Institute of Information Technology, New Delhi, she explored the role of different context cues in improving computational hate speech-related tasks
Session
As large language models (LLMs)-powered “AI highlights” become the first information people see on the Web, a key question arises: how much variety and perspective do these systems actually deliver for information-seeking queries? Do LLMs offer broader viewpoints than traditional search or Wikipedia pages? Do larger models really produce more diverse answers—or are they all converging on the same language, and framing, raising concerns about “knowledge collapse”?
Drawing insights from experiments across LLM families, real-world topics, and hundreds of user-style prompts, this talk introduces an open-source framework for benchmarking and tracking epistemic diversity in LLMs. We focus on practical lessons for data scientists building and evaluating LLM-powered search, summaries, and knowledge systems—where diversity of information actually matters.