1 citations · 1 across the 10 of their papers we have counts for
11 papers
Moir: Let the Model Direct Its Own Story for Robust Cross-Domain Knowledge Editing
Jea Kwon, Jiwon Kim, Dong-kyum Kim +1
While language models remain frozen at their training state, the world evolves continuously. Knowledge editing has emerged as a key alternative to full retraining, but its deployme…
AI Engram: In Search of Memory Traces in Artificial Intelligence
Jea Kwon, Dong-Kyum Kim, Jiwon Kim +3
Memory formation is fundamental to intelligence, yet whether deep neural networks preserve identifiable memory traces analogous to biological memory units remains an open question.…
Textual Supervision Enhances Geospatial Representations in Vision-Language Models
Marcelo Sartori Locatelli, Fernando Tonucci, Jea Kwon +5
Geospatial understanding is a critical yet underexplored dimension in the development of machine learning systems for tasks such as image geolocation and spatial reasoning. In this…
Machine Behavior in Relational Moral Dilemmas: Moral Rightness, Predicted Human Behavior, and Model Decisions
Jiseon Kim, Jea Kwon, Luiz Felipe Vecchietti +3
Human moral judgment is context-dependent and modulated by interpersonal relationships. As large language models (LLMs) increasingly function as decision-support systems, determini…
Dropouts in Confidence: Moral Uncertainty in Human-LLM Alignment
Jea Kwon, Luiz Felipe Vecchietti, Sungwon Park +1
Humans display significant uncertainty when confronted with moral dilemmas, yet the extent of such uncertainty in machines and AI agents remains underexplored. Recent studies have…
How Training Data Shapes the Use of Parametric and In-Context Knowledge in Language Models
Minsung Kim, Dong-Kyum Kim, Jea Kwon +3
Large language models leverage both parametric knowledge acquired during pretraining and in-context knowledge provided at inference time. Crucially, when these sources conflict, mo…
Erase or Hide? Suppressing Spurious Unlearning Neurons for Robust Unlearning
Nakyeong Yang, Dong-Kyum Kim, Jea Kwon +3
Large language models trained on web-scale data can memorize private or sensitive knowledge, raising significant privacy risks. Although some unlearning methods mitigate these risk…
Bilinear representation mitigates reversal curse and enables consistent model editing
Dong-Kyum Kim, Minsung Kim, Jea Kwon +2
The reversal curse--a language model's inability to infer an unseen fact "B is A" from a learned fact "A is B"--is widely considered a fundamental limitation. We show that this is…
Exploring Persona-dependent LLM Alignment for the Moral Machine Experiment
Jiseon Kim, Jea Kwon, Luiz Felipe Vecchietti +2
Deploying large language models (LLMs) with agency in real-world applications raises critical questions about how these models will behave. In particular, how will their decisions…
Recent advances in interpretable machine learning using structure-based protein representations
Luiz Felipe Vecchietti, Minji Lee, Begench Hangeldiyev +5
Recent advancements in machine learning (ML) are transforming the field of structural biology. For example, AlphaFold, a groundbreaking neural network for protein structure predict…
Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space
Minji Lee, Luiz Felipe Vecchietti, Hyunkyu Jung +3
Proteins are complex molecules responsible for different functions in nature. Enhancing the functionality of proteins and cellular fitness can significantly impact various industri…