127 citations · 512 across the 55 of their papers we have counts for
57 papers
Why, Where, How: Taxonomy-guided Error Grounding for Code Repair in NL2SQL
Suchan Lee, Woomin Song, Hwanjo Yu +1
SQL queries that large language models write from natural language questions can execute successfully yet produce incorrect results, so execution alone does not reveal what to fix.…
Difficulty-Adaptive Tree-Structured Policy Optimization for Expanding Reasoning Coverage in RLVR
Youngjun Yu, Sanghwan Jang, Hwanjo Yu
Reinforcement Learning with Verifiable Rewards (RLVR) has been central to the recent success of Large Reasoning Models. However, while RLVR significantly improves single-sample acc…
Personalized and Multi-View Representation for Federated Cold-Start Recommendation
Jaehyung Lim, Wonbin Kweon, Woojoo Kim +3
Federated recommendation (FedRec) enables personalized modeling without centralizing users' interaction histories, but most existing methods assume a fixed item pool and thus overl…
RA-VLA: Retrieval-Augmented VLA for Test-Time Adaptation
Sanghwan Jang, Minjin Jeon, Minsoo Kim +3
Vision-Language-Action (VLA) models provide a versatile foundation for general robotic manipulation, yet they exhibit significant brittleness when confronted with novel task distri…
TRACER: Balancing Stability-Plasticity-Cognitivity Trilemma for LLM Enhanced Continual Recommendation
WooJoo Kim, HyunSik Yoo, JunYoung Kim +3
Continual recommendation aims to capture evolving user interests from streaming data but struggles with sparsity. LLM enhancers mitigate this with semantic knowledge, but naive int…
GOD: Enhancing Generalization via Deep Grafting for Sequential Recommendation
WooJoo Kim, JunYoung Kim, JaeHyung Lim +1
Sequential recommenders often struggle with sparse and noisy histories, limiting generalization to unseen interactions. Knowledge distillation mitigates this by transferring dense…
ReWEIGH the Evidence: Calibrating Token-Level Ordinal Visual Evidence to Mitigate Hallucinations in Large Vision-Language Models
Jihae Jeong, Junha Choi, Hwanjo Yu
Large vision-language models (LVLMs) often hallucinate, generating content that the input image does not support. Preventing such content during decoding calls for a candidate-spec…
From Overlooked to Explored: Recovering Item Relations via Mixture of Perspectives for Sequential Recommendation
Junyoung Kim, Wonbin Kweon, Woojoo Kim +3
Capturing user preference from a user's interaction sequence is the central challenge of Sequential Recommendation (SR). This preference intuitively emerges from inter-item relatio…
Valid Necessary: Diagnosing Latent Inefficiency in Chain-of-Thought
Daeyeop Lee, Hwanjo Yu
Chain-of-Thought (CoT) prompting has significantly advanced the reasoning capabilities of Large Language Models (LLMs), yet it often incurs substantial computational costs due to o…
FLAME: Condensing Ensemble Diversity into a Single Network for Efficient Sequential Recommendation
WooJoo Kim, JunYoung Kim, JaeHyung Lim +3
Sequential recommendation requires capturing diverse user behaviors, which a single network often fails to capture. While ensemble methods mitigate this, training multiple networks…
VLM2Rec: Resolving Modality Collapse in Vision-Language Model Embedders for Multimodal Sequential Recommendation
Junyoung Kim, Woojoo Kim, Wonbin Kweon +3
Sequential Recommendation (SR) in multimodal settings typically relies on small frozen pretrained encoders, which limits semantic capacity and prevents Collaborative Filtering (CF)…
Improving Scientific Document Retrieval with Academic Concept Index
Jeyun Lee, Junhyoung Lee, Wonbin Kweon +7
Adapting general-domain retrievers to scientific domains is challenging due to the scarcity of large-scale domain-specific relevance annotations and the substantial mismatch in voc…
Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures
Tyler A. Chang, Catherine Arnett, Abdelrahman Sadallah +377
To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we pre…
MAP4TS: A Multi-Aspect Prompting Framework for Time-Series Forecasting with Large Language Models
Suchan Lee, Jihoon Choi, Sohyeon Lee +4
Recent advances have investigated the use of pretrained large language models (LLMs) for time-series forecasting by aligning numerical inputs with LLM embedding spaces. However, ex…
Continual Collaborative Distillation for Recommender System
Gyuseok Lee, SeongKu Kang, Wonbin Kweon +1
Knowledge distillation (KD) has emerged as a promising technique for addressing the computational challenges associated with deploying large-scale recommender systems. KD transfers…
Learning Discriminative Dynamics with Label Corruption for Noisy Label Detection
Suyeon Kim, Dongha Lee, SeongKu Kang +3
Label noise, commonly found in real-world datasets, has a detrimental impact on a model's generalization. To effectively detect incorrectly labeled instances, previous works have m…
Rectifying Demonstration Shortcut in In-Context Learning
Joonwon Jang, Sanghwan Jang, Wonbin Kweon +2
Large language models (LLMs) are able to solve various tasks with only a few demonstrations utilizing their in-context learning (ICL) abilities. However, LLMs often rely on their p…
Multi-Domain Recommendation to Attract Users via Domain Preference Modeling
Hyunjun Ju, SeongKu Kang, Dongha Lee +3
Recently, web platforms have been operating various service domains simultaneously. Targeting a platform that operates multiple service domains, we introduce a new task, Multi-Doma…
Exploring Language Model's Code Generation Ability with Auxiliary Functions
Seonghyeon Lee, Sanghwan Jang, Seongbo Jang +2
Auxiliary function is a helpful component to improve language model's code generation ability. However, a systematic exploration of how they affect has yet to be done. In this work…
Improving Matrix Completion by Exploiting Rating Ordinality in Graph Neural Networks
Jaehyun Lee, SeongKu Kang, Hwanjo Yu
Matrix completion is an important area of research in recommender systems. Recent methods view a rating matrix as a user-item bi-partite graph with labeled edges denoting observed…
Improving Retrieval in Theme-specific Applications using a Corpus Topical Taxonomy
SeongKu Kang, Shivam Agarwal, Bowen Jin +3
Document retrieval has greatly benefited from the advancements of large-scale pre-trained language models (PLMs). However, their effectiveness is often limited in theme-specific ap…
Doubly Calibrated Estimator for Recommendation on Data Missing Not At Random
Wonbin Kweon, Hwanjo Yu
Recommender systems often suffer from selection bias as users tend to rate their preferred items. The datasets collected under such conditions exhibit entries missing not at random…
Deep Rating Elicitation for New Users in Collaborative Filtering
Wonbin Kweon, SeongKu Kang, Junyoung Hwang +1
Recent recommender systems started to use rating elicitation, which asks new users to rate a small seed itemset for inferring their preferences, to improve the quality of initial r…
Top-Personalized-K Recommendation
Wonbin Kweon, SeongKu Kang, Sanghwan Jang +1
The conventional top-K recommendation, which presents the top-K items with the highest ranking scores, is a common practice for generating personalized ranking lists. However, is t…