1 citations · 3 across the 9 of their papers we have counts for
9 papers
MERIT Feedback Elicits Better Bargaining in LLM Negotiators
Jihwan Oh, Murad Aghazada, Yooju Shin +2
Bargaining is often regarded as a logical arena rather than an art or a matter of intuition, yet Large Language Models (LLMs) still struggle to navigate it due to limited strategic…
LLM Agents for Bargaining with Utility-based Feedback
Jihwan Oh
Bargaining, a critical aspect of real-world interactions, presents challenges for large language models (LLMs) due to limitations in strategic depth and adaptation to complex human…
Guiding Reasoning in Small Language Models with LLM Assistance
Yujin Kim, Euiin Yi, Minu Kim +2
The limited reasoning capabilities of small language models (SLMs) cast doubt on their suitability for tasks demanding deep, multi-step logical deduction. This paper introduces a f…
Conditional Synthesis of 3D Molecules with Time Correction Sampler
Hojung Jung, Youngrok Park, Laura Schmid +5
Diffusion models have demonstrated remarkable success in various domains, including molecular generation. However, conditional molecular generation remains a fundamental challenge…
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…
Leveraging Normalization Layer in Adapters With Progressive Learning and Adaptive Distillation for Cross-Domain Few-Shot Learning
Yongjin Yang, Taehyeon Kim, Se-Young Yun
Cross-domain few-shot learning presents a formidable challenge, as models must be trained on base classes and then tested on novel classes from various domains with only a few samp…