152 citations · 276 across the 36 of their papers we have counts for
37 papers
Foundations of Proactive Agents: Principles, Technical Layers, and Proactivity-Gym
Jio Oh, Seunghyun Do, Young-Jun Lee +2
Proactive LLM agents can turn idle compute into useful support before users ask. Yet even correct work can misread user context, impose review costs, or undermine trust. This work…
Q-TIE: A Lightweight and Generalizable Re-ranking Framework for Temporal Information Retrieval
Soyeon Kim, Hyunjin Kim, JinYeong Bak +1
Temporal Information Retrieval (TIR) has been increasingly critical given the rise of Retrieval-Augmented Generation (RAG). Since temporally mismatched evidence can be highly misle…
Preference Data Selection for Mitigating the Alignment Tax in Large Language Models
Minsu Kim, Jianxun Lian, Xing Xie +1
Aligning large language models to human preferences is crucial for real-world deployment but frequently incurs an alignment tax, leading to the catastrophic forgetting of pre-train…
Multimodal Federated Learning under Dual-Axis Modality Missingness
Adiba Orzikulova, Jaehyun Kwak, Jaemin Shin +5
Multimodal federated learning (FL) supports collaborative modeling in privacy-sensitive health-sensing and medical settings, but realistic deployments often exhibit dual-axis modal…
DeFrame: Debiasing Large Language Models Against Framing Effects
Kahee Lim, Soyeon Kim, Steven Euijong Whang
As large language models (LLMs) are increasingly deployed in real-world applications, ensuring their fair responses across demographics has become crucial. Despite many efforts, an…
Explanation Multiplicity in SHAP: Characterization and Assessment
Hyunseung Hwang, Seungeun Lee, Lucas Rosenblatt +2
SHAP explanations are widely used in high-stakes settings to justify decisions, yet they can differ substantially across repeated runs, even when the model, the input instance, and…
DialectLLM: A Dialect-Aware Dialog[ue] Generation Framework Beyond Standard American English
Jio Oh, Paul Vicinanza, Thomas Butler +3
More than 80% of the 1.6B English speakers do not use Standard American English (SAE), yet LLMs often fail to correctly identify non-SAE dialects and generate stereotyped responses…
Classroom AI: Large Language Models as Grade-Specific Teachers
Jio Oh, Steven Euijong Whang, James Evans +1
Large Language Models (LLMs) offer a promising solution to complement traditional teaching and address global teacher shortages that affect hundreds of millions of children, but th…
MIDAS: Misalignment-based Data Augmentation Strategy for Imbalanced Multimodal Learning
Seong-Hyeon Hwang, Soyoung Choi, Steven Euijong Whang
Multimodal models often over-rely on dominant modalities, failing to achieve optimal performance. While prior work focuses on modifying training objectives or optimization procedur…
Harnessing Temporal Databases for Systematic Evaluation of Factual Time-Sensitive Question-Answering in Large Language Models
Soyeon Kim, Jindong Wang, Xing Xie +1
Facts change over time, making it essential for Large Language Models (LLMs) to handle time-sensitive factual knowledge accurately and reliably. Although factual Time-Sensitive Que…
SHAP-based Explanations are Sensitive to Feature Representation
Hyunseung Hwang, Andrew Bell, Joao Fonseca +3
Local feature-based explanations are a key component of the XAI toolkit. These explanations compute feature importance values relative to an ``interpretable'' feature representatio…
GradMix: Gradient-based Selective Mixup for Robust Data Augmentation in Class-Incremental Learning
Minsu Kim, Seong-Hyeon Hwang, Steven Euijong Whang
In the context of continual learning, acquiring new knowledge while maintaining previous knowledge presents a significant challenge. Existing methods often use experience replay te…
T-CIL: Temperature Scaling using Adversarial Perturbation for Calibration in Class-Incremental Learning
Seong-Hyeon Hwang, Minsu Kim, Steven Euijong Whang
We study model confidence calibration in class-incremental learning, where models learn from sequential tasks with different class sets. While existing works primarily focus on acc…
Talking with Tables for Better LLM Factual Data Interactions
Jio Oh, Geon Heo, Seungjun Oh +5
Large Language Models (LLMs) often struggle with requests related to information retrieval and data manipulation that frequently arise in real-world scenarios under multiple condit…
PFGuard: A Generative Framework with Privacy and Fairness Safeguards
Soyeon Kim, Yuji Roh, Geon Heo +1
Generative models must ensure both privacy and fairness for Trustworthy AI. While these goals have been pursued separately, recent studies propose to combine existing privacy and f…
Fair Class-Incremental Learning using Sample Weighting
Jaeyoung Park, Minsu Kim, Steven Euijong Whang
Model fairness is becoming important in class-incremental learning for Trustworthy AI. While accuracy has been a central focus in class-incremental learning, fairness has been rela…
RC-Mixup: A Data Augmentation Strategy against Noisy Data for Regression Tasks
Seong-Hyeon Hwang, Minsu Kim, Steven Euijong Whang
We study the problem of robust data augmentation for regression tasks in the presence of noisy data. Data augmentation is essential for generalizing deep learning models, but most…
ERBench: An Entity-Relationship based Automatically Verifiable Hallucination Benchmark for Large Language Models
Jio Oh, Soyeon Kim, Junseok Seo +4
Large language models (LLMs) have achieved unprecedented performances in various applications, yet evaluating them is still challenging. Existing benchmarks are either manually con…
LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views
Yuji Roh, Qingyun Liu, Huan Gui +8
Fine-tuning is becoming widely used for leveraging the power of pre-trained foundation models in new downstream tasks. While there are many successes of fine-tuning on various task…
Falcon: Fair Active Learning using Multi-armed Bandits
Ki Hyun Tae, Hantian Zhang, Jaeyoung Park +2
Biased data can lead to unfair machine learning models, highlighting the importance of embedding fairness at the beginning of data analysis, particularly during dataset curation an…
Quilt: Robust Data Segment Selection against Concept Drifts
Minsu Kim, Seong-Hyeon Hwang, Steven Euijong Whang
Continuous machine learning pipelines are common in industrial settings where models are periodically trained on data streams. Unfortunately, concept drifts may occur in data strea…
Personalized DP-SGD using Sampling Mechanisms
Geon Heo, Junseok Seo, Steven Euijong Whang
Personalized privacy becomes critical in deep learning for Trustworthy AI. While Differentially Private Stochastic Gradient Descent (DP-SGD) is widely used in deep learning methods…
Improving Fair Training under Correlation Shifts
Yuji Roh, Kangwook Lee, Steven Euijong Whang +1
Model fairness is an essential element for Trustworthy AI. While many techniques for model fairness have been proposed, most of them assume that the training and deployment data di…
iFlipper: Label Flipping for Individual Fairness
Hantian Zhang, Ki Hyun Tae, Jaeyoung Park +2
As machine learning becomes prevalent, mitigating any unfairness present in the training data becomes critical. Among the various notions of fairness, this paper focuses on the wel…