Seungwon Lim
Hi, I'm Seungwon Lim. I'm a researcher at Yonsei University, MIRLAB (Multimodal Intelligance Research Lab) advised by Youngjae Yu. I received my bachelor's degree in Computer Science, and I am currently pursuing an integrated MS/PhD program in Computer Science.
My Research question I want to develop is "What lies behind AI systems' actions and utterances?". I believe their actions and utterances cannot be attributed solely to computation, but can be analyzed through humanities, sociology, and linguistics. I'm particularly interested in language as the connection between AI systems and fields studying human experience.
Publications
VisEscape: A Benchmark for Evaluating Exploration-driven Decision-making in Virtual Escape Rooms
Under Review
TLDR; We introduce VisEscape inspired by Escape Room games, and evaluate the Reasoning and Decision-making of diverse MLLMs in exploration-driven and dynamic environments.
Persona Dynamics: Unveiling the Impact of Persona Traits on Agents in Text-Based Games
ACL2025 Main
TLDR; We introduce PANDA, which incorporates Human Personality Traits into AI agents for Text-based Games and examines how these traits impact their behavior and performance.
Do LLMs Have Distinct and Consistent Personality? TRAIT: Personality Testset designed for LLMs with Psychometrics
NAACL2025 Findings
TLDR; We introduce a psychometric-based benchmark TRAIT to measure the personality revealed in the Behavior Patterns of LLMs along with verification of Reliability and Validity.
MASS: Overcoming Language Bias in Image-Text Matching
AAAI2025 Main
TLDR; We introduce MASS, a Training-free framework that improves Visual Accuracy and reduces Bias in Image-Text Matching for pretrained visual-language models.
Can visual language models resolve textual ambiguity with visual cues? Let visual puns tell you!
EMNLP2024 Main
TLDR; We introduce UNPIE, a new benchmark crafted to evaluate how multimodal inputs influence the Resolution of Lexical Ambiguities.
CLARA: Classifying and Disambiguating User Commands for Reliable Interactive Robotic Agents
ICRA2024
TLDR; We introduce CLARA, a LLM-empowered method for robots to estimate Uncertainty of user commands and to Disambiguate them via question generation for clarification.