12 papers
PRAGMA: Evaluating Personalized Guidance with Memory Alignment in Lifelong Conversations
Hyojeong Yu, Hyukhun Koh, Minsung Kim +2
Large language models (LLMs) are increasingly deployed as personalized assistants that interact with users over extended periods of time. As conversations grow longer, relying on f…
Casual as an Anchor: Resolving Supervision Misalignment in Formality Transfer Dataset
Hyojeong Yu, Hyukhun Koh, Minsung Kim +1
Formality transfer is commonly framed as a symmetric bidirectional task between informal and formal registers. We argue that this framing conceals a supervision design flaw in exis…
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…
Rethinking Post-Unlearning Behavior of Large Vision-Language Models
Minsung Kim, Nakyeong Yang, Kyomin Jung
Large Vision-Language Models (LVLMs) can recognize individuals in images and disclose sensitive personal information about them, raising critical privacy concerns. Machine unlearni…
FaithUn: Toward Faithful Forgetting in Language Models by Investigating the Interconnectedness of Knowledge
Nakyeong Yang, Minsung Kim, Seunghyun Yoon +2
Various studies have attempted to remove sensitive or private knowledge from a language model to prevent its unauthorized exposure. However, prior studies have overlooked the compl…
Generating Diverse Hypotheses for Inductive Reasoning
Kang-il Lee, Hyukhun Koh, Dongryeol Lee +3
Inductive reasoning - the process of inferring general rules from a small number of observations - is a fundamental aspect of human intelligence. Recent works suggest that large la…
Fine-grained Gender Control in Machine Translation with Large Language Models
Minwoo Lee, Hyukhun Koh, Minsung Kim +1
In machine translation, the problem of ambiguously gendered input has been pointed out, where the gender of an entity is not available in the source sentence. To address this ambig…
VLind-Bench: Measuring Language Priors in Large Vision-Language Models
Kang-il Lee, Minbeom Kim, Seunghyun Yoon +4
Large Vision-Language Models (LVLMs) have demonstrated outstanding performance across various multimodal tasks. However, they suffer from a problem known as language prior, where r…
MVMR: A New Framework for Evaluating Faithfulness of Video Moment Retrieval against Multiple Distractors
Nakyeong Yang, Minsung Kim, Seunghyun Yoon +2
With the explosion of multimedia content, video moment retrieval (VMR), which aims to detect a video moment that matches a given text query from a video, has been studied intensive…
Target-Agnostic Gender-Aware Contrastive Learning for Mitigating Bias in Multilingual Machine Translation
Minwoo Lee, Hyukhun Koh, Kang-il Lee +3
Gender bias is a significant issue in machine translation, leading to ongoing research efforts in developing bias mitigation techniques. However, most works focus on debiasing bili…