1 citations · 1 across the 5 of their papers we have counts for
17 papers
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…
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…
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…