1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.CY2026
LLMs as Oracles: Reliance on LLMs for Subjective Personal Questions
Myra Cheng, Lujain Ibrahim, Grace Liu +5
We characterize how people are turning to LLMs as oracles: all-knowing authorities on subjective personal questions. Motivated by risks to users' autonomy and well-being, we develo…
cs.HC2026
Behavior Latticing: Inferring User Motivations from Unstructured Interactions
Dora Zhao, Michelle S. Lam, Diyi Yang +1
A long-standing vision of computing is the personal AI system: one that understands us well enough to address our underlying needs. Today's AI focuses on what users do, ignoring wh…
cs.HC2024★ 1 cited
Policy Maps: Tools for Guiding the Unbounded Space of LLM Behaviors
Michelle S. Lam, Fred Hohman, Dominik Moritz +3
AI policy sets boundaries on acceptable behavior for AI models, but this is challenging in the context of large language models (LLMs): how do you ensure coverage over a vast behav…