9 citations · 15 across the 8 of their papers we have counts for
3 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…
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…
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…