28 citations · 57 across the 11 of their papers we have counts for
7 papers · 1 filter
Correcting Negative Bias in Large Language Models through Negative Attention Score Alignment
Sangwon Yu, Jongyoon Song, Bongkyu Hwang +7
A binary decision task, like yes-no questions or answer verification, reflects a significant real-world scenario such as where users look for confirmation about the correctness of…
Entity-level Factual Adaptiveness of Fine-tuning based Abstractive Summarization Models
Jongyoon Song, Nohil Park, Bongkyu Hwang +4
Abstractive summarization models often generate factually inconsistent content particularly when the parametric knowledge of the model conflicts with the knowledge in the input doc…
Shuffle & Divide: Contrastive Learning for Long Text
Joonseok Lee, Seongho Joe, Kyoungwon Park +4
We propose a self-supervised learning method for long text documents based on contrastive learning. A key to our method is Shuffle and Divide (SaD), a simple text augmentation algo…
Enhancing Semantic Understanding with Self-supervised Methods for Abstractive Dialogue Summarization
Hyunjae Lee, Jaewoong Yun, Hyunjin Choi +2
Contextualized word embeddings can lead to state-of-the-art performances in natural language understanding. Recently, a pre-trained deep contextualized text encoder such as BERT ha…
KoreALBERT: Pretraining a Lite BERT Model for Korean Language Understanding
Hyunjae Lee, Jaewoong Yoon, Bonggyu Hwang +3
A Lite BERT (ALBERT) has been introduced to scale up deep bidirectional representation learning for natural languages. Due to the lack of pretrained ALBERT models for Korean langua…
Analyzing Zero-shot Cross-lingual Transfer in Supervised NLP Tasks
Hyunjin Choi, Judong Kim, Seongho Joe +2
In zero-shot cross-lingual transfer, a supervised NLP task trained on a corpus in one language is directly applicable to another language without any additional training. A source…
Evaluation of BERT and ALBERT Sentence Embedding Performance on Downstream NLP Tasks
Hyunjin Choi, Judong Kim, Seongho Joe +1
Contextualized representations from a pre-trained language model are central to achieve a high performance on downstream NLP task. The pre-trained BERT and A Lite BERT (ALBERT) mod…