15 citations · 25 across the 8 of their papers we have counts for
5 papers · 1 filter
Epistemic diversity across language models mitigates knowledge collapse
Damian Hodel, Jevin D. West
Artificial intelligence (AI) increasingly generates the very content used to train future AI systems. This feedback loop can degrade model quality, reduce informational diversity,…
The Role of Follow Networks and Twitter's Content Recommender on Partisan Skew and Rumor Exposure during the 2022 U.S. Midterm Election
Kayla Duskin, Joseph S. Schafer, Alexandros Efstratiou +2
Social media platforms shape users' experiences through the algorithmic systems they deploy. In this study, we examine to what extent Twitter's content recommender, in conjunction…
From job titles to jawlines: Using context voids to study generative AI systems
Shahan Ali Memon, Soham De, Sungha Kang +5
In this paper, we introduce a speculative design methodology for studying the behavior of generative AI systems, framing design as a mode of inquiry. We propose bridging seemingly…
An Empirical Study on Zero-Data Bootstrapping for Conversational Recommender Systems
Rohan Surana, Junda Wu, Zhouhang Xie +3
Conversational Recommender Systems (CRS) typically require domain-specific dialogue data, which is costly, scarce, and often unavailable in new domains. We conduct a systematic emp…
Deep Graph Learning will stall without Network Science
Christopher Blöcker, Martin Rosvall, Ingo Scholtes +1
Deep graph learning focuses on flexible and generalizable models that learn patterns in an automated fashion. Network science focuses on models and measures revealing the organizat…