Plug-in and Fine-tuning: Bridging the Gap between Small Language Models and Large Language Models

ACL 2025 Plug-in and Fine-tuning: Bridging the Gap between Small Language Models and Large Language Models  Kyeonghyun Kim, Jinhee Jang, Juhwan Choi, Yoonji Lee, Kyohoon Jin, YoungBin Kim  Large language models (LLMs) are renowned for their extensive linguistic knowledge and...

Delving into Multilingual Ethical Bias: The MSQAD with Statistical Hypothesis Tests for Large Language Models

ACL 2025 Delving into Multilingual Ethical Bias: The MSQAD with Statistical Hypothesis Tests for Large Language Models  Seunguk Yu, Juhwan Choi, Youngbin Kim  Despite the recent strides in large language models, studies have underscored the existence of social biases within ...

Augmenting Document Representations for Dense Retrieval with Interpolation and Perturbation

ACL 2022 Augmenting Document Representations for Dense Retrieval with Interpolation and Perturbation Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, Jong C. Park  Dense retrieval models, which aim at retrieving the most relevant document for an input query on a dense repr...

Learning to Perturb Word Embeddings for Out-of-distribution QA

ACL 2021 Learning to Perturb Word Embeddings for Out-of-distribution QA Seanie Lee, Minki Kang, Juho Lee and Sung Ju Hwang  QA models based on pretrained language models have achieved remarkable performance on various benchmark datasets. However, QA models do not generalize well ...

Episodic Memory Reader: Learning What to Remember for Question Answering from Streaming Data

ACL 2019 Episodic Memory Reader: Learning What to Remember for Question Answering from Streaming DataMoonsu Han, Minki Kang, Hyunwoo Jung, Sung Ju Hwang We consider a novel question answering (QA) task where the machine needs to read from large streaming data (long documents or v...