110 citations · 336 across the 32 of their papers we have counts for
9 papers · 1 filter
Intuitive Access to Smartphone Settings Using Relevance Model Trained by Contrastive Learning
Joonyoung Kim, Kangwook Lee, Haebin Shin +5
The more new features that are being added to smartphones, the harder it becomes for users to find them. This is because the feature names are usually short, and there are just too…
Teaching Arithmetic to Small Transformers
Nayoung Lee, Kartik Sreenivasan, Jason D. Lee +2
Large language models like GPT-4 exhibit emergent capabilities across general-purpose tasks, such as basic arithmetic, when trained on extensive text data, even though these tasks…
Predictive Pipelined Decoding: A Compute-Latency Trade-off for Exact LLM Decoding
Seongjun Yang, Gibbeum Lee, Jaewoong Cho +2
This paper presents "Predictive Pipelined Decoding (PPD)," an approach that speeds up greedy decoding in Large Language Models (LLMs) while maintaining the exact same output as the…
Prompted LLMs as Chatbot Modules for Long Open-domain Conversation
Gibbeum Lee, Volker Hartmann, Jongho Park +2
In this paper, we propose MPC (Modular Prompted Chatbot), a new approach for creating high-quality conversational agents without the need for fine-tuning. Our method utilizes pre-t…
Variation Spaces for Multi-Output Neural Networks: Insights on Multi-Task Learning and Network Compression
Joseph Shenouda, Rahul Parhi, Kangwook Lee +1
This paper introduces a novel theoretical framework for the analysis of vector-valued neural networks through the development of vector-valued variation spaces, a new class of repr…
DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models
Ying Fan, Olivia Watkins, Yuqing Du +7
Learning from human feedback has been shown to improve text-to-image models. These techniques first learn a reward function that captures what humans care about in the task and the…
Improving Fair Training under Correlation Shifts
Yuji Roh, Kangwook Lee, Steven Euijong Whang +1
Model fairness is an essential element for Trustworthy AI. While many techniques for model fairness have been proposed, most of them assume that the training and deployment data di…
Looped Transformers as Programmable Computers
Angeliki Giannou, Shashank Rajput, Jy-yong Sohn +3
We present a framework for using transformer networks as universal computers by programming them with specific weights and placing them in a loop. Our input sequence acts as a punc…
Optimizing DDPM Sampling with Shortcut Fine-Tuning
Ying Fan, Kangwook Lee
In this study, we propose Shortcut Fine-Tuning (SFT), a new approach for addressing the challenge of fast sampling of pretrained Denoising Diffusion Probabilistic Models (DDPMs). S…