Beyond Single-User Dialogue: Assessing Multi-User Dialogue State Tracking Capabilities of Large Language Models

Beyond Single-User Dialogue: Assessing Multi-User Dialogue State Tracking Capabilities of Large Language ModelsSangmin Song, Juhwan Choi, JungMin Yun, YoungBin KimLarge language models (LLMs) have demonstrated remarkable performance in zero-shot dialogue state tracking (DST), reducing the need for t...

CoBA: Counterbias Text Augmentation for Mitigating Various Spurious Correlations via Semantic Triples

  Accepted at EMNLP 2025CoBA: Counterbias Text Augmentation for Mitigating Various Spurious Correlations via Semantic TriplesKyohoon Jin, Juhwan Choi, Jungmin Yun, Junho Lee, Soojin Jang, Youngbin KimDeep learning models often learn and exploit spurious correlations in training data, using...

PromptKD: Distilling Student-Friendly Knowledge for Generative Language Models via Prompt Tuning

Findings of the Conference on Empirical Methods in Natural Language Processing (Findings of EMNLP) 2024 PromptKD: Distilling Student-Friendly Knowledge for Generative Language Models via Prompt Tuning Gyeongman Kim, Doohyuk Jang, Eunho Yang  Recent advancements in large lang...

Distilling Linguistic Context for Language Model Compression

EMNLP 2021 Distilling Linguistic Context for Language Model Compression Geondo Park, Gyeongman Kim, Eunho Yang  A computationally expensive and memory intensive neural network lies behind the recent success of language representation learning. Knowledge distillation, a major tech...

Neural Mask Generator: Learning to Generate Adaptive Word Maskings for Language Model Adaptation

EMNLP 2020 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 pr...