29 citations · 36 across the 7 of their papers we have counts for
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cs.CL2025
Show or Tell? Modeling the evolution of request-making in Human-LLM conversations
Shengqi Zhu, Jeffrey M. Rzeszotarski, David Mimno
Designing user-centered LLM systems requires understanding how people use them, but patterns of user behavior are often masked by the variability of queries. In this work, we intro…
cs.HC2025
Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systems
Jing Nathan Yan, Emma Harvey, Junxiong Wang +2
Recommender systems (RS), which are widely deployed across high-stakes domains, are susceptible to biases that can cause large-scale societal impacts. Researchers have proposed met…