2 citations · 3 across the 14 of their papers we have counts for
15 papers
K-EXAONE 2.0 Technical Report
Eunbi Choi, Kibong Choi, Sehyun Chun +74
This technical report presents K-EXAONE 2.0, an open-weight multilingual foundation model developed by LG AI Research as a step in our effort toward global frontier-scale foundatio…
OPRO: Orthogonal Panel-Relative Operators for Panel-Aware In-Context Image Generation
Sanghyeon Lee, Minwoo Lee, Euijin Shin +3
We introduce a parameter-efficient adaptation method for panel-aware in-context image generation with pre-trained diffusion transformers. The key idea is to compose learnable, pane…
SNAP: Speaker Nulling for Artifact Projection in Speech Deepfake Detection
Kyudan Jung, Jihwan Kim, Minwoo Lee +4
Recent advancements in text-to-speech technologies enable generating high-fidelity synthetic speech nearly indistinguishable from real human voices. While recent studies show the e…
When Wording Steers the Evaluation: Framing Bias in LLM judges
Yerin Hwang, Dongryeol Lee, Taegwan Kang +2
Large language models (LLMs) are known to produce varying responses depending on prompt phrasing, indicating that subtle guidance in phrasing can steer their answers. However, the…
Judging Against the Reference: Uncovering Knowledge-Driven Failures in LLM-Judges on QA Evaluation
Dongryeol Lee, Yerin Hwang, Taegwan Kang +3
While large language models (LLMs) are increasingly used as automatic judges for question answering (QA) and other reference-conditioned evaluation tasks, little is known about the…
Think Before You Prune: Self-Reflective Structured Pruning for Reasoning Language Models
Ziyan Wang, Enmao Diao, Qi Le +5
Reasoning LLMs (RLMs) such as OpenAI o1, DeepSeek-R1, and Qwen3 deliver strong multi-step reasoning through chain-of-thought generation, but their large model sizes and lengthy dec…
From Local to Global: Revisiting Structured Pruning Paradigms for Large Language Models
Ziyan Wang, Enmao Diao, Qi Le +6
Structured pruning is a practical approach to deploying large language models (LLMs) efficiently, as it yields compact, hardware-friendly architectures. However, the dominant local…
Synthetic Tabular Data Generation: A Comparative Survey for Modern Techniques
Raju Challagundla, Mohsen Dorodchi, Pu Wang +1
As privacy regulations become more stringent and access to real-world data becomes increasingly constrained, synthetic data generation has emerged as a vital solution, especially f…
Fine-grained Gender Control in Machine Translation with Large Language Models
Minwoo Lee, Hyukhun Koh, Minsung Kim +1
In machine translation, the problem of ambiguously gendered input has been pointed out, where the gender of an entity is not available in the source sentence. To address this ambig…
Return of EM: Entity-driven Answer Set Expansion for QA Evaluation
Dongryeol Lee, Minwoo Lee, Kyungmin Min +2
Recently, directly using large language models (LLMs) has been shown to be the most reliable method to evaluate QA models. However, it suffers from limited interpretability, high c…
BAMM: Bidirectional Autoregressive Motion Model
Ekkasit Pinyoanuntapong, Muhammad Usama Saleem, Pu Wang +3
Generating human motion from text has been dominated by denoising motion models either through diffusion or generative masking process. However, these models face great limitations…
Can LLMs Recognize Toxicity? A Structured Investigation Framework and Toxicity Metric
Hyukhun Koh, Dohyung Kim, Minwoo Lee +1
In the pursuit of developing Large Language Models (LLMs) that adhere to societal standards, it is imperative to detect the toxicity in the generated text. The majority of existing…
MMM: Generative Masked Motion Model
Ekkasit Pinyoanuntapong, Pu Wang, Minwoo Lee +1
Recent advances in text-to-motion generation using diffusion and autoregressive models have shown promising results. However, these models often suffer from a trade-off between rea…
MutualNet: Adaptive ConvNet via Mutual Learning from Different Model Configurations
Taojiannan Yang, Sijie Zhu, Matias Mendieta +6
Most existing deep neural networks are static, which means they can only do inference at a fixed complexity. But the resource budget can vary substantially across different devices…
Drug-disease Graph: Predicting Adverse Drug Reaction Signals via Graph Neural Network with Clinical Data
Heeyoung Kwak, Minwoo Lee, Seunghyun Yoon +3
Adverse Drug Reaction (ADR) is a significant public health concern world-wide. Numerous graph-based methods have been applied to biomedical graphs for predicting ADRs in pre-market…