179 citations · 1k across the 84 of their papers we have counts for
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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…
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…
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…
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…
Language Detoxification with Attribute-Discriminative Latent Space
Jin Myung Kwak, Minseon Kim, Sung Ju Hwang
Transformer-based Language Models (LMs) have achieved impressive results on natural language understanding tasks, but they can also generate toxic text such as insults, threats, an…
KALA: Knowledge-Augmented Language Model Adaptation
Minki Kang, Jinheon Baek, Sung Ju Hwang
Pre-trained language models (PLMs) have achieved remarkable success on various natural language understanding tasks. Simple fine-tuning of PLMs, on the other hand, might be subopti…
Contrastive Learning with Adversarial Perturbations for Conditional Text Generation
Seanie Lee, Dong Bok Lee, Sung Ju Hwang
Recently, sequence-to-sequence (seq2seq) models with the Transformer architecture have achieved remarkable performance on various conditional text generation tasks, such as machine…
Neural Mask Generator: Learning to Generate Adaptive Word Maskings for Language Model Adaptation
Minki Kang, Moonsu Han, Sung Ju Hwang
We propose a method to automatically generate a domain- and task-adaptive maskings of the given text for self-supervised pre-training, such that we can effectively adapt the langua…
Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs
Dong Bok Lee, Seanie Lee, Woo Tae Jeong +2
One of the most crucial challenges in question answering (QA) is the scarcity of labeled data, since it is costly to obtain question-answer (QA) pairs for a target text domain with…