Judging What We Cannot Solve: A Consequence-Based Approach for Oracle-Free Evaluation of Research-Level Math
Guijin Son · Donghun Yang · Hitesh Patel · Hyunwoo Ko · Amit Agarwal · Sunghee Ahn · Kyong-Ha Lee · Youngjae Yu
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Guijin Son · Donghun Yang · Hitesh Patel · Hyunwoo Ko · Amit Agarwal · Sunghee Ahn · Kyong-Ha Lee · Youngjae Yu
Junhyeok Kim · Jaewoo Park · Junhee Park · Sangeyl Lee · Jiwan Chung · Jisung Kim · Ji Hoon Joung · Youngjae Yu
For people affected by blindness and low vision (BLV), safe and independent navigation remains a major challenge, impacting over 2.2 billion individuals worldwide. Although multimodal large language models (MLLMs) offer new opportunities for assistive navigation, progress has been limited by the...
Jinhong Jeong · Junghun Park · Youngjae Yu
Text simplification supports second language (L2) learning by providing comprehensible input, consistent with the Input Hypothesis. However, constructing personalized parallel corpora is costly, while existing large language model (LLM)-based readability control methods rely on pre-labeled senten...
Shubin Kim · Yejin Son · Junyeong Park · Keummin Ka · Seungbeen Lee · Jaeyoung Lee · Hyeju Jang · Alice Oh · Youngjae Yu
Humor holds up a mirror to social perception: what we find funny often reflects who we are and how we judge others. When language models engage with humor, their reactions expose the social assumptions they have internalized from training data. In this paper, we investigate counterfactual unfairn...
Jaehyun Jeon · Min Soo Kim · Janghan Yoon · Sumin Shim · Yejin Choi · Hanbin Kim · Dae Hyun Kim · Youngjae Yu
User interface (UI) design goes beyond visuals to shape user experience (UX), underscoring the shift toward UI/UX as a unified concept. While recent studies have explored UI evaluation using Multimodal Large Language Models (MLLMs), they largely focus on surface-level features, overlooking how de...
Jungyang Park · Suho Kang · Jaewoo Park · Jae Hong Kim · Jaewoo Shin · Seonjoon Park · Youngjae Yu
Knowledge Tracing (KT) aims to model student’s knowledge state and predict future performance to enable personalized learning in Intelligent Tutoring Systems. However, traditional KT methods face fundamental limitations in explainability, as they rely solely on the response correctness, neglectin...
Wonje Jeung · Sangyeon Yoon · Hyesoo Hong · Soeun Kim · Seungju Han · Youngjae Yu · Albert No
Machine unlearning aims to remove “forget” data while preserving knowledge from the “retain” data, yet a fundamental question arises when the two share content. By definition, an unlearned model should be indistinguishable from a model retrained solely on the retain set, which implies that shared...