3 papers
cs.HC2025
Interaction Context Often Increases Sycophancy in LLMs
Shomik Jain, Charlotte Park, Matt Viana +2
We investigate how the presence and type of interaction context shapes sycophancy in LLMs. While real-world interactions allow models to mirror a user's values, preferences, and se…
cs.GT2025
Homogeneous Algorithms Can Reduce Competition in Personalized Pricing
Nathanael Jo, Kathleen Creel, Ashia Wilson +1
Firms' algorithm development practices are often homogeneous. Whether firms train algorithms on similar data, aim at similar benchmarks, or rely on similar pre-trained models, the…
cs.CY2025
Allocation Multiplicity: Evaluating the Promises of the Rashomon Set
Shomik Jain, Margaret Wang, Kathleen Creel +1
The Rashomon set of equally-good models promises less discriminatory algorithms, reduced outcome homogenization, and fairer decisions through model ensembles or reconciliation. How…