34 citations · 56 across the 67 of their papers we have counts for
15 papers · 1 filter
Improving Generative Behavior Cloning via Self-Guidance and Adaptive Chunking
Junhyuk So, Chiwoong Lee, Shinyoung Lee +2
Generative Behavior Cloning (GBC) is a simple yet effective framework for robot learning, particularly in multi-task settings. Recent GBC methods often employ diffusion policies wi…
Adaptive Planning for Multi-Attribute Controllable Summarization with Monte Carlo Tree Search
Sangwon Ryu, Heejin Do, Yunsu Kim +2
Controllable summarization moves beyond generic outputs toward human-aligned summaries guided by specified attributes. In practice, the interdependence among attributes makes it ch…
LLM Watermark Evasion via Bias Inversion
Jeongyeon Hwang, Sangdon Park, Jungseul Ok
Watermarking offers a promising solution for detecting LLM-generated content, yet its robustness under realistic query-free (black-box) evasion remains an open challenge. Existing…
Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark
Suyeon Kim, SeongKu Kang, Dongwoo Kim +2
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in node classification tasks but struggle with label noise in real-world data. Existing studies on graph lea…
DeRAGEC: Denoising Named Entity Candidates with Synthetic Rationale for ASR Error Correction
Solee Im, Wonjun Lee, Jinmyeong An +3
We present DeRAGEC, a method for improving Named Entity (NE) correction in Automatic Speech Recognition (ASR) systems. By extending the Retrieval-Augmented Generative Error Correct…
Influence Functions for Edge Edits in Non-Convex Graph Neural Networks
Jaeseung Heo, Kyeongheung Yun, Seokwon Yoon +3
Understanding how individual edges influence the behavior of graph neural networks (GNNs) is essential for improving their interpretability and robustness. Graph influence function…
Efficient Latent Semantic Clustering for Scaling Test-Time Computation of LLMs
Sungjae Lee, Hoyoung Kim, Jeongyeon Hwang +2
Scaling test-time computation--generating and analyzing multiple or sequential outputs for a single input--has become a promising strategy for improving the reliability and quality…
Self-Training Large Language Models with Confident Reasoning
Hyosoon Jang, Yunhui Jang, Sungjae Lee +2
Large language models (LLMs) have shown impressive performance by generating reasoning paths before final answers, but learning such a reasoning path requires costly human supervis…
Experience-based Knowledge Correction for Robust Planning in Minecraft
Seungjoon Lee, Suhwan Kim, Minhyeon Oh +2
Large Language Model (LLM)-based planning has advanced embodied agents in long-horizon environments such as Minecraft, where acquiring latent knowledge of goal (or item) dependenci…
MiLQ: Benchmarking IR Models for Bilingual Web Search with Mixed Language Queries
Jonghwi Kim, Deokhyung Kang, Seonjeong Hwang +3
Despite bilingual speakers frequently using mixed-language queries in web searches, Information Retrieval (IR) research on them remains scarce. To address this, we introduce MiLQ,…
Revisiting Early Detection of Sexual Predators via Turn-level Optimization
Jinmyeong An, Sangwon Ryu, Heejin Do +3
Online grooming is a severe social threat where sexual predators gradually entrap child victims with subtle and gradual manipulation. Therefore, timely intervention for online groo…
CoPL: Collaborative Preference Learning for Personalizing LLMs
Youngbin Choi, Seunghyuk Cho, Minjong Lee +4
Personalizing large language models (LLMs) is important for aligning outputs with diverse user preferences, yet existing methods struggle with flexibility and generalization. We pr…
DyPCL: Dynamic Phoneme-level Contrastive Learning for Dysarthric Speech Recognition
Wonjun Lee, Solee Im, Heejin Do +3
Dysarthric speech recognition often suffers from performance degradation due to the intrinsic diversity of dysarthric severity and extrinsic disparity from normal speech. To bridge…
Enhancing Cost Efficiency in Active Learning with Candidate Set Query
Yeho Gwon, Sehyun Hwang, Hoyoung Kim +2
This paper introduces a cost-efficient active learning (AL) framework for classification, featuring a novel query design called candidate set query. Unlike traditional AL queries r…
Semantic Exploration with Adaptive Gating for Efficient Problem Solving with Language Models
Sungjae Lee, Hyejin Park, Jaechang Kim +1
Recent advancements in large language models (LLMs) have shown remarkable potential in various complex tasks requiring multi-step reasoning methods like tree search to explore dive…