38 citations · 79 across the 23 of their papers we have counts for
5 papers · 1 filter
: Scalable Auto-Feedback for LLM-based Chart Generation
Woosung Koh, Jang Han Yoon, MinHyung Lee +7
Generating high-quality charts with Large Language Models (LLMs) presents significant challenges due to limited data and the high cost of scaling through human curation. $\langle \…
Towards Fast Multilingual LLM Inference: Speculative Decoding and Specialized Drafters
Euiin Yi, Taehyeon Kim, Hongseok Jeung +2
Large language models (LLMs) have revolutionized natural language processing and broadened their applicability across diverse commercial applications. However, the deployment of th…
Block Transformer: Global-to-Local Language Modeling for Fast Inference
Namgyu Ho, Sangmin Bae, Taehyeon Kim +6
We introduce the Block Transformer which adopts hierarchical global-to-local modeling to autoregressive transformers to mitigate the inference bottlenecks associated with self-atte…
Revisiting Early-Learning Regularization When Federated Learning Meets Noisy Labels
Taehyeon Kim, Donggyu Kim, Se-Young Yun
In the evolving landscape of federated learning (FL), addressing label noise presents unique challenges due to the decentralized and diverse nature of data collection across client…
Hypernetwork-Driven Model Fusion for Federated Domain Generalization
Marc Bartholet, Taehyeon Kim, Ami Beuret +2
Federated Learning (FL) faces significant challenges with domain shifts in heterogeneous data, degrading performance. Traditional domain generalization aims to learn domain-invaria…