Byeonghu Na

WASD: Wasserstein-based Knowledge Distillation for Large Language Models

Byeonghu Na, Donghyeok Shin, Yeongmin Kim +2

Autoregressive large language models (LLMs) have rapidly advanced in capability, but their increasing scale comes with substantial computational and memory costs at inference time.…

Multi-Agent Debate and Visual Information Extraction for SeePhys Pro: A 1st-Place Technical Report from ICML 2026 AI4Math Track 3 Challenge

Jiseok Kwak, Suhyeon Jo, Taewoo Kim +3

This technical report presents our approach to Challenge Track~3: SeePhys Pro at the 3rd AI for Math Workshop, where the task is to answer college-level physics questions whose sta…

Missing Pattern Recognized Diffusion Imputation Model for Missing Not At Random

Gyuwon Sim, Sumin Lee, Heesun Bae +5

Missing data frequently arises across diverse domains, including time-series and image domains. In the real world, missing occurrences often depend on the unobservable values thems…

Semantic-aware Wasserstein Policy Regularization for Large Language Model Alignment

Byeonghu Na, Hyungho Na, Yeongmin Kim +4

Large language models (LLMs) are commonly aligned with human preferences using reinforcement learning from human feedback (RLHF). In this method, LLM policies are generally optimiz…

Lookahead Sample Reward Guidance for Test-Time Scaling of Diffusion Models

Yeongmin Kim, Donghyeok Shin, Byeonghu Na +3

Diffusion models have demonstrated strong generative performance; however, generated samples often fail to fully align with human intent. This paper studies an efficient test-time…

Prompt-Based Safety Guidance Is Ineffective for Unlearned Text-to-Image Diffusion Models

Jiwoo Shin, Byeonghu Na, Mina Kang +2

Recent advances in text-to-image generative models have raised concerns about their potential to produce harmful content when provided with malicious input text prompts. To address…

Training-Free Safe Text Embedding Guidance for Text-to-Image Diffusion Models

Byeonghu Na, Mina Kang, Jiseok Kwak +6

Text-to-image models have recently made significant advances in generating realistic and semantically coherent images, driven by advanced diffusion models and large-scale web-crawl…

Diffusion Adaptive Text Embedding for Text-to-Image Diffusion Models

Byeonghu Na, Minsang Park, Gyuwon Sim +6

Text-to-image diffusion models rely on text embeddings from a pre-trained text encoder, but these embeddings remain fixed across all diffusion timesteps, limiting their adaptabilit…

AMiD: Knowledge Distillation for LLMs with $α$ -mixture Assistant Distribution

Donghyeok Shin, Yeongmin Kim, Suhyeon Jo +2

Autoregressive large language models (LLMs) have achieved remarkable improvement across many tasks but incur high computational and memory costs. Knowledge distillation (KD) mitiga…

Distillation of Large Language Models via Concrete Score Matching

Yeongmin Kim, Donghyeok Shin, Mina Kang +2

Large language models (LLMs) deliver remarkable performance but are costly to deploy, motivating knowledge distillation (KD) for efficient inference. Existing KD objectives typical…

Preference Optimization by Estimating the Ratio of the Data Distribution

Yeongmin Kim, Heesun Bae, Byeonghu Na +1

Direct preference optimization (DPO) is widely used as a simple and stable method for aligning large language models (LLMs) with human preferences. This paper investigates a genera…

cs.CL2024

Reward-based Input Construction for Cross-document Relation Extraction

Byeonghu Na, Suhyeon Jo, Yeongmin Kim +1

Relation extraction (RE) is a fundamental task in natural language processing, aiming to identify relations between target entities in text. While many RE methods are designed for…

Diffusion Rejection Sampling

Byeonghu Na, Yeongmin Kim, Minsang Park +3

Recent advances in powerful pre-trained diffusion models encourage the development of methods to improve the sampling performance under well-trained diffusion models. This paper in…

Diffusion Bridge AutoEncoders for Unsupervised Representation Learning

Yeongmin Kim, Kwanghyeon Lee, Minsang Park +2

Diffusion-based representation learning has achieved substantial attention due to its promising capabilities in latent representation and sample generation. Recent studies have emp…

cs.LG2024★ 1 cited

Unknown Domain Inconsistency Minimization for Domain Generalization

Seungjae Shin, HeeSun Bae, Byeonghu Na +2

The objective of domain generalization (DG) is to enhance the transferability of the model learned from a source domain to unobserved domains. To prevent overfitting to a specific…

Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning

HeeSun Bae, Seungjae Shin, Byeonghu Na +1

For learning with noisy labels, the transition matrix, which explicitly models the relation between noisy label distribution and clean label distribution, has been utilized to achi…

Training Unbiased Diffusion Models From Biased Dataset

Yeongmin Kim, Byeonghu Na, Minsang Park +4

With significant advancements in diffusion models, addressing the potential risks of dataset bias becomes increasingly important. Since generated outputs directly suffer from datas…