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Hyeonmin Ha

3 accepted papers

2023

Meta-Learning of Prompt Generation for Lightweight Prompt Engineering on Language-Model-as-a-Service

EMNLP 2023long findings

Recently, many companies have been providing the capabilities of large language models as services. These Language-Model-as-a-Service (LMaaS) offerings support a variety of user tasks through in-context learning from prompts, which include instructions and demonstrations of the task. However, for us…

Cited by 0SourceScholar
2023

Two Examples are Better than One: Context Regularization for Gradient-based Prompt Tuning

ACL 2023findings

Prompting has gained tremendous attention as an efficient method for the adaptation of large-scale language models. However, prompts often act against human intuition and report unstable performances, which has motivated methods that automatically find effective prompts. One popular approach is grad…

Cited by 1SourcePDFScholar
2022

SUMNAS: Supernet with Unbiased Meta-Features for Neural Architecture Search

ICLR 2022poster

One-shot Neural Architecture Search (NAS) usually constructs an over-parameterized network, which we call a supernet, and typically adopts sharing parameters among the sub-models to improve computational efficiency. One-shot NAS often repeatedly samples sub-models from the supernet and trains them t…

Cited by 6SourcePDFScholar