3 citations · 5 across the 13 of their papers we have counts for
6 papers · 1 filter
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…
INSIDE the Student's Mind: Jointly Modeling Latent Reasoning and Action in LLM Student Simulators
Rose Niousha, Minwoo Kang, Narges Norouzi
Large Language Model (LLM)-based simulators often reproduce observable actions but fail to capture the underlying reasoning behind them. In education, where student simulation is i…
Speculative Interaction Agents: Building Real-Time Agents with Asynchronous I/O and Speculative Tool Calling
Coleman Hooper, Minwoo Kang, Suhong Moon +7
There is a growing demand for agentic AI technologies for a range of downstream applications like customer service and personal assistants. For applications where the agent needs t…
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…
Dynamic In-Group Persona Generation for Enhancing Human-AI Rapport
Yoonseok Oh, Inseo Jung, Jinkyu Kim +3
LLM-based chatbots are increasingly applied in interpersonal domains such as counseling and peer support, where establishing human-AI rapport is crucial yet remains challenging. In…
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…