2 citations · 5 across the 67 of their papers we have counts for
18 papers · 1 filter
Sparse autoencoders reveal selective remapping of visual concepts during adaptation
Hyesu Lim, Jinho Choi, Jaegul Choo +1
Adapting foundation models for specific purposes has become a standard approach to build machine learning systems for downstream applications. Yet, it is an open question which mec…
Forecasting Future International Events: A Reliable Dataset for Text-Based Event Modeling
Daehoon Gwak, Junwoo Park, Minho Park +4
Predicting future international events from textual information, such as news articles, has tremendous potential for applications in global policy, strategic decision-making, and g…
Breaking Chains: Unraveling the Links in Multi-Hop Knowledge Unlearning
Minseok Choi, ChaeHun Park, Dohyun Lee +1
Large language models (LLMs) serve as giant information stores, often including personal or copyrighted data, and retraining them from scratch is not a viable option. This has led…
Evaluating Automatic Speech Recognition Systems for Korean Meteorological Experts
ChaeHun Park, Hojun Cho, Jaegul Choo
This paper explores integrating Automatic Speech Recognition (ASR) into natural language query systems to improve weather forecasting efficiency for Korean meteorologists. We addre…
Imagining the Unseen: Generative Location Modeling for Object Placement
Jooyeol Yun, Davide Abati, Mohamed Omran +3
Location modeling, or determining where non-existing objects could feasibly appear in a scene, has the potential to benefit numerous computer vision tasks, from automatic object in…
Bones Can't Be Triangles: Accurate and Efficient Vertebrae Keypoint Estimation through Collaborative Error Revision
Jinhee Kim, Taesung Kim, Jaegul Choo
Recent advances in interactive keypoint estimation methods have enhanced accuracy while minimizing user intervention. However, these methods require user input for error correction…
Training Spatial-Frequency Visual Prompts and Probabilistic Clusters for Accurate Black-Box Transfer Learning
Wonwoo Cho, Kangyeol Kim, Saemee Choi +1
Despite the growing prevalence of black-box pre-trained models (PTMs) such as prediction API services, there remains a significant challenge in directly applying general models to…
Pacer and Runner: Cooperative Learning Framework between Single- and Cross-Domain Sequential Recommendation
Chung Park, Taesan Kim, Hyungjun Yoon +5
Cross-Domain Sequential Recommendation (CDSR) improves recommendation performance by utilizing information from multiple domains, which contrasts with Single-Domain Sequential Reco…
Is user feedback always informative? Retrieval Latent Defending for Semi-Supervised Domain Adaptation without Source Data
Junha Song, Tae Soo Kim, Junha Kim +3
This paper aims to adapt the source model to the target environment, leveraging small user feedback (i.e., labeled target data) readily available in real-world applications. We fin…
Layout-and-Retouch: A Dual-stage Framework for Improving Diversity in Personalized Image Generation
Kangyeol Kim, Wooseok Seo, Sehyun Nam +5
Personalized text-to-image (P-T2I) generation aims to create new, text-guided images featuring the personalized subject with a few reference images. However, balancing the trade-of…
Protecting Privacy Through Approximating Optimal Parameters for Sequence Unlearning in Language Models
Dohyun Lee, Daniel Rim, Minseok Choi +1
Although language models (LMs) demonstrate exceptional capabilities on various tasks, they are potentially vulnerable to extraction attacks, which represent a significant privacy r…
Translation Deserves Better: Analyzing Translation Artifacts in Cross-lingual Visual Question Answering
ChaeHun Park, Koanho Lee, Hyesu Lim +5
Building a reliable visual question answering~(VQA) system across different languages is a challenging problem, primarily due to the lack of abundant samples for training. To addre…
Evaluating Visual and Cultural Interpretation: The K-Viscuit Benchmark with Human-VLM Collaboration
ChaeHun Park, Yujin Baek, Jaeseok Kim +3
To create culturally inclusive vision-language models (VLMs), developing a benchmark that tests their ability to address culturally relevant questions is essential. Existing approa…
Cross-Lingual Unlearning of Selective Knowledge in Multilingual Language Models
Minseok Choi, Kyunghyun Min, Jaegul Choo
Pretrained language models memorize vast amounts of information, including private and copyrighted data, raising significant safety concerns. Retraining these models after excludin…
Opt-Out: Investigating Entity-Level Unlearning for Large Language Models via Optimal Transport
Minseok Choi, Daniel Rim, Dohyun Lee +1
Instruction-following large language models (LLMs), such as ChatGPT, have become widely popular among everyday users. However, these models inadvertently disclose private, sensitiv…
The Comparative Trap: Pairwise Comparisons Amplifies Biased Preferences of LLM Evaluators
Hawon Jeong, ChaeHun Park, Jimin Hong +2
As large language models (LLMs) are increasingly used as evaluators for natural language generation tasks, ensuring unbiased assessments is essential. However, LLM evaluators often…
Can Tool-augmented Large Language Models be Aware of Incomplete Conditions?
Seungbin Yang, ChaeHun Park, Taehee Kim +1
Recent advancements in integrating large language models (LLMs) with tools have allowed the models to interact with real-world environments. However, these tool-augmented LLMs ofte…
PairEval: Open-domain Dialogue Evaluation with Pairwise Comparison
ChaeHun Park, Minseok Choi, Dohyun Lee +1
Building a reliable and automated evaluation metric is a necessary but challenging problem for open-domain dialogue systems. Recent studies proposed evaluation metrics that assess…