35 citations · 81 across the 27 of their papers we have counts for
6 papers · 1 filter
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…
Revisiting the Practical Effectiveness of Constituency Parse Extraction from Pre-trained Language Models
Taeuk Kim
Constituency Parse Extraction from Pre-trained Language Models (CPE-PLM) is a recent paradigm that attempts to induce constituency parse trees relying only on the internal knowledg…
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…
HYU at SemEval-2022 Task 2: Effective Idiomaticity Detection with Consideration at Different Levels of Contextualization
Youngju Joung, Taeuk Kim
We propose a unified framework that enables us to consider various aspects of contextualization at different levels to better identify the idiomaticity of multi-word expressions. T…
Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations
Kang Min Yoo, Junyeob Kim, Hyuhng Joon Kim +5
Despite recent explosion of interests in in-context learning, the underlying mechanism and the precise impact of the quality of demonstrations remain elusive. Intuitively, ground-t…