21 citations · 41 across the 12 of their papers we have counts for
13 papers
When Identity Skews Debate: Anonymization for Bias-Reduced Multi-Agent Reasoning
Hyeong Kyu Choi, Xiaojin Zhu, Sharon Li
Multi-agent debate (MAD) aims to improve large language model (LLM) reasoning by letting multiple agents exchange answers and then aggregate their opinions. Yet recent studies reve…
Detecting Distillation Data from Reasoning Models
Hengxiang Zhang, Hyeong Kyu Choi, Sharon Li +1
Reasoning distillation has emerged as a prevailing paradigm for transferring reasoning capabilities from large reasoning models to small language models. Yet, reasoning distillatio…
Debate or Vote: Which Yields Better Decisions in Multi-Agent Large Language Models?
Hyeong Kyu Choi, Xiaojin Zhu, Sharon Li
Multi-Agent Debate~(MAD) has emerged as a promising paradigm for improving the performance of large language models through collaborative reasoning. Despite recent advances, the ke…
How Contaminated Is Your Benchmark? Quantifying Dataset Leakage in Large Language Models with Kernel Divergence
Hyeong Kyu Choi, Maxim Khanov, Hongxin Wei +1
Dataset contamination, where evaluation datasets overlap with pre-training corpora, inflates performance metrics and undermines the reliability of model evaluations. Measuring data…
Safety-Aware Fine-Tuning of Large Language Models
Hyeong Kyu Choi, Xuefeng Du, Yixuan Li
Fine-tuning Large Language Models (LLMs) has emerged as a common practice for tailoring models to individual needs and preferences. The choice of datasets for fine-tuning can be di…
Mitigating Selection Bias with Node Pruning and Auxiliary Options
Hyeong Kyu Choi, Weijie Xu, Chi Xue +2
Large language models (LLMs) often exhibit systematic preferences for certain answer choices when responding to multiple-choice questions-a behavior known as selection bias. This b…
PICLe: Eliciting Diverse Behaviors from Large Language Models with Persona In-Context Learning
Hyeong Kyu Choi, Yixuan Li
Large Language Models (LLMs) are trained on massive text corpora, which are encoded with diverse personality traits. This triggers an interesting goal of eliciting a desired person…
NuTrea: Neural Tree Search for Context-guided Multi-hop KGQA
Hyeong Kyu Choi, Seunghun Lee, Jaewon Chu +1
Multi-hop Knowledge Graph Question Answering (KGQA) is a task that involves retrieving nodes from a knowledge graph (KG) to answer natural language questions. Recent GNN-based appr…
MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models
Dohwan Ko, Joonmyung Choi, Hyeong Kyu Choi +3
Foundation models have shown outstanding performance and generalization capabilities across domains. Since most studies on foundation models mainly focus on the pretraining phase,…
Relation-Aware Language-Graph Transformer for Question Answering
Jinyoung Park, Hyeong Kyu Choi, Juyeon Ko +5
Question Answering (QA) is a task that entails reasoning over natural language contexts, and many relevant works augment language models (LMs) with graph neural networks (GNNs) to…
TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers
Hyeong Kyu Choi, Joonmyung Choi, Hyunwoo J. Kim
Mixup is a commonly adopted data augmentation technique for image classification. Recent advances in mixup methods primarily focus on mixing based on saliency. However, many salien…
Consistency Learning via Decoding Path Augmentation for Transformers in Human Object Interaction Detection
Jihwan Park, SeungJun Lee, Hwan Heo +2
Human-Object Interaction detection is a holistic visual recognition task that entails object detection as well as interaction classification. Previous works of HOI detection has be…
Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Model
Hyeong Kyu Choi
Predicting the price correlation of two assets for future time periods is important in portfolio optimization. We apply LSTM recurrent neural networks (RNN) in predicting the stock…