3 citations · 5 across the 13 of their papers we have counts for
5 papers · 1 filter
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…