3 citations · 5 across the 13 of their papers we have counts for
13 papers
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…
Puzzled by Puzzles: When Vision-Language Models Can't Take a Hint
Heekyung Lee, Jiaxin Ge, Tsung-Han Wu +3
Rebus puzzles, visual riddles that encode language through imagery, spatial arrangement, and symbolic substitution, pose a unique challenge to current vision-language models (VLMs)…
Deep Binding of Language Model Virtual Personas: a Study on Approximating Political Partisan Misperceptions
Minwoo Kang, Suhong Moon, Seung Hyeong Lee +4
Large language models (LLMs) are increasingly capable of simulating human behavior, offering cost-effective ways to estimate user responses to various surveys and polls. However, t…
High-Throughput SAT Sampling
Arash Ardakani, Minwoo Kang, Kevin He +2
In this work, we present a novel technique for GPU-accelerated Boolean satisfiability (SAT) sampling. Unlike conventional sampling algorithms that directly operate on conjunctive n…
DEMOTIC: A Differentiable Sampler for Multi-Level Digital Circuits
Arash Ardakani, Minwoo Kang, Kevin He +4
Efficient sampling of satisfying formulas for circuit satisfiability (CircuitSAT), a well-known NP-complete problem, is essential in modern front-end applications for thorough test…
Language Model Fine-Tuning on Scaled Survey Data for Predicting Distributions of Public Opinions
Joseph Suh, Erfan Jahanparast, Suhong Moon +2
Large language models (LLMs) present novel opportunities in public opinion research by predicting survey responses in advance during the early stages of survey design. Prior method…
Rediscovering the Latent Dimensions of Personality with Large Language Models as Trait Descriptors
Joseph Suh, Suhong Moon, Minwoo Kang +1
Assessing personality traits using large language models (LLMs) has emerged as an interesting and challenging area of research. While previous methods employ explicit questionnaire…
Virtual Personas for Language Models via an Anthology of Backstories
Suhong Moon, Marwa Abdulhai, Minwoo Kang +5
Large language models (LLMs) are trained from vast repositories of text authored by millions of distinct authors, reflecting an enormous diversity of human traits. While these mode…