179 citations · 1k across the 84 of their papers we have counts for
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Continual Learners are Incremental Model Generalizers
Jaehong Yoon, Sung Ju Hwang, Yue Cao
Motivated by the efficiency and rapid convergence of pre-trained models for solving downstream tasks, this paper extensively studies the impact of Continual Learning (CL) models as…
Phrase Retrieval for Open-Domain Conversational Question Answering with Conversational Dependency Modeling via Contrastive Learning
Soyeong Jeong, Jinheon Baek, Sung Ju Hwang +1
Open-Domain Conversational Question Answering (ODConvQA) aims at answering questions through a multi-turn conversation based on a retriever-reader pipeline, which retrieves passage…
Progressive Fourier Neural Representation for Sequential Video Compilation
Haeyong Kang, Jaehong Yoon, DaHyun Kim +2
Neural Implicit Representation (NIR) has recently gained significant attention due to its remarkable ability to encode complex and high-dimensional data into representation space a…
Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation
Minki Kang, Jin Myung Kwak, Jinheon Baek +1
Language models have achieved impressive performances on dialogue generation tasks. However, when generating responses for a conversation that requires factual knowledge, they are…
A Study on Knowledge Distillation from Weak Teacher for Scaling Up Pre-trained Language Models
Hayeon Lee, Rui Hou, Jongpil Kim +3
Distillation from Weak Teacher (DWT) is a method of transferring knowledge from a smaller, weaker teacher model to a larger student model to improve its performance. Previous studi…
Meta-prediction Model for Distillation-Aware NAS on Unseen Datasets
Hayeon Lee, Sohyun An, Minseon Kim +1
Distillation-aware Neural Architecture Search (DaNAS) aims to search for an optimal student architecture that obtains the best performance and/or efficiency when distilling the kno…
Direct Fact Retrieval from Knowledge Graphs without Entity Linking
Jinheon Baek, Alham Fikri Aji, Jens Lehmann +1
There has been a surge of interest in utilizing Knowledge Graphs (KGs) for various natural language processing/understanding tasks. The conventional mechanism to retrieve facts in…
Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Tasks
Minki Kang, Seanie Lee, Jinheon Baek +2
Large Language Models (LLMs) have shown promising performance in knowledge-intensive reasoning tasks that require a compound understanding of knowledge. However, deployment of the…
Set-based Neural Network Encoding Without Weight Tying
Bruno Andreis, Soro Bedionita, Philip H. S. Torr +1
We propose a neural network weight encoding method for network property prediction that utilizes set-to-set and set-to-vector functions to efficiently encode neural network paramet…
Text-Conditioned Sampling Framework for Text-to-Image Generation with Masked Generative Models
Jaewoong Lee, Sangwon Jang, Jaehyeong Jo +5
Token-based masked generative models are gaining popularity for their fast inference time with parallel decoding. While recent token-based approaches achieve competitive performanc…
Forget-free Continual Learning with Soft-Winning SubNetworks
Haeyong Kang, Jaehong Yoon, Sultan Rizky Madjid +2
Inspired by Regularized Lottery Ticket Hypothesis (RLTH), which states that competitive smooth (non-binary) subnetworks exist within a dense network in continual learning tasks, we…
Realistic Conversational Question Answering with Answer Selection based on Calibrated Confidence and Uncertainty Measurement
Soyeong Jeong, Jinheon Baek, Sung Ju Hwang +1
Conversational Question Answering (ConvQA) models aim at answering a question with its relevant paragraph and previous question-answer pairs that occurred during conversation multi…
Graph Generation with Diffusion Mixture
Jaehyeong Jo, Dongki Kim, Sung Ju Hwang
Generation of graphs is a major challenge for real-world tasks that require understanding the complex nature of their non-Euclidean structures. Although diffusion models have achie…