3 citations · 3 across the 9 of their papers we have counts for
5 papers · 1 filter
Dataset Signatures in Human-LLM Interactions and User Modeling
Joseph Suh, Serina Chang
Human--LLM interaction datasets shape our understanding of AI use and provide a foundation for downstream research, including training and evaluation of user models. In recent year…
Pretrained Persona Mixture Models and Tandem Models for Human Simulation
Minwoo Kang, Téa Wright, Seun Eisape +6
We argue here that the current dominant practice in LLM human simulation: prompting instruction-tuned assistant language models to role-play personas, is inaccurate and produces st…
Anamnesis: An Open-Source Platform for Large-Scale Backstory-Conditioned Survey Simulation
Song-Ze Yu, Joseph Suh, Serina Chang +1
We present Anamnesis, an interactive system for demographically controllable survey simulation using large language models. Open-source and designed for non-technical users/researc…
Quantifying the Utility of User Simulators for Building Collaborative LLM Assistants
Joseph Suh, Ayush Raj, Minwoo Kang +1
User simulators are increasingly leveraged to build interactive AI assistants, yet how to measure the quality of these simulators remains an open question. In this work, we show ho…
Identity, Cooperation and Framing Effects within Groups of Real and Simulated Humans
Suhong Moon, Minwoo Kang, Joseph Suh +2
Humans act via a nuanced process that depends both on rational deliberation and also on identity and contextual factors. In this work, we study how large language models (LLMs) can…