35 citations · 72 across the 13 of their papers we have counts for
11 papers · 1 filter
CELDA: Leveraging Black-box Language Model as Enhanced Classifier without Labels
Hyunsoo Cho, Youna Kim, Sang-goo Lee
Utilizing language models (LMs) without internal access is becoming an attractive paradigm in the field of NLP as many cutting-edge LMs are released through APIs and boast a massiv…
Probing Out-of-Distribution Robustness of Language Models with Parameter-Efficient Transfer Learning
Hyunsoo Cho, Choonghyun Park, Junyeop Kim +3
As the size of the pre-trained language model (PLM) continues to increase, numerous parameter-efficient transfer learning methods have been proposed recently to compensate for the…
Prompt-Augmented Linear Probing: Scaling beyond the Limit of Few-shot In-Context Learners
Hyunsoo Cho, Hyuhng Joon Kim, Junyeob Kim +4
Through in-context learning (ICL), large-scale language models are effective few-shot learners without additional model fine-tuning. However, the ICL performance does not scale wel…
Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble
Hyunsoo Cho, Choonghyun Park, Jaewook Kang +3
Out-of-distribution (OOD) detection aims to discern outliers from the intended data distribution, which is crucial to maintaining high reliability and a good user experience. Most…
Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator
Hyuhng Joon Kim, Hyunsoo Cho, Junyeob Kim +3
Large-scale pre-trained language models (PLMs) are well-known for being capable of solving a task simply by conditioning a few input-label pairs dubbed demonstrations on a prompt w…
IDS at SemEval-2020 Task 10: Does Pre-trained Language Model Know What to Emphasize?
Jaeyoul Shin, Taeuk Kim, Sang-goo Lee
We propose a novel method that enables us to determine words that deserve to be emphasized from written text in visual media, relying only on the information from the self-attentio…
Are Pre-trained Language Models Aware of Phrases? Simple but Strong Baselines for Grammar Induction
Taeuk Kim, Jihun Choi, Daniel Edmiston +1
With the recent success and popularity of pre-trained language models (LMs) in natural language processing, there has been a rise in efforts to understand their inner workings. In…
Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation
Kang Min Yoo, Hanbit Lee, Franck Dernoncourt +3
Recent works have shown that generative data augmentation, where synthetic samples generated from deep generative models complement the training dataset, benefit NLP tasks. In this…
Summary Level Training of Sentence Rewriting for Abstractive Summarization
Sanghwan Bae, Taeuk Kim, Jihoon Kim +1
As an attempt to combine extractive and abstractive summarization, Sentence Rewriting models adopt the strategy of extracting salient sentences from a document first and then parap…
Don't Just Scratch the Surface: Enhancing Word Representations for Korean with Hanja
Kang Min Yoo, Taeuk Kim, Sang-goo Lee
We propose a simple yet effective approach for improving Korean word representations using additional linguistic annotation (i.e. Hanja). We employ cross-lingual transfer learning…
A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching
Jihun Choi, Taeuk Kim, Sang-goo Lee
We present a latent variable model for predicting the relationship between a pair of text sequences. Unlike previous auto-encoding--based approaches that consider each sequence sep…