@inproceedings{yoo-etal-2022-ground,
title = "Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations",
author = "Yoo, Kang Min and
Kim, Junyeob and
Kim, Hyuhng Joon and
Cho, Hyunsoo and
Jo, Hwiyeol and
Lee, Sang-Woo and
Lee, Sang-goo and
Kim, Taeuk",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.155",
doi = "10.18653/v1/2022.emnlp-main.155",
pages = "2422--2437",
abstract = "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-truth labels should have as much impact in in-context learning (ICL) as supervised learning, but recent work reported that the input-label correspondence is significantly less important than previously thought. Intrigued by this counter-intuitive observation, we re-examine the importance of ground-truth labels in in-context learning. With the introduction of two novel metrics, namely Label-Correctness Sensitivity and Ground-truth Label Effect Ratio (GLER), we were able to conduct quantifiable analysis on the impact of ground-truth label demonstrations. Through extensive analyses, we find that the correct input-label mappings can have varying impacts on the downstream in-context learning performances, depending on the experimental configuration. Through additional studies, we identify key components, such as the verbosity of prompt templates and the language model size, as the controlling factor to achieve more noise-resilient ICL.",
}
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<abstract>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-truth labels should have as much impact in in-context learning (ICL) as supervised learning, but recent work reported that the input-label correspondence is significantly less important than previously thought. Intrigued by this counter-intuitive observation, we re-examine the importance of ground-truth labels in in-context learning. With the introduction of two novel metrics, namely Label-Correctness Sensitivity and Ground-truth Label Effect Ratio (GLER), we were able to conduct quantifiable analysis on the impact of ground-truth label demonstrations. Through extensive analyses, we find that the correct input-label mappings can have varying impacts on the downstream in-context learning performances, depending on the experimental configuration. Through additional studies, we identify key components, such as the verbosity of prompt templates and the language model size, as the controlling factor to achieve more noise-resilient ICL.</abstract>
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%0 Conference Proceedings
%T Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations
%A Yoo, Kang Min
%A Kim, Junyeob
%A Kim, Hyuhng Joon
%A Cho, Hyunsoo
%A Jo, Hwiyeol
%A Lee, Sang-Woo
%A Lee, Sang-goo
%A Kim, Taeuk
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F yoo-etal-2022-ground
%X 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-truth labels should have as much impact in in-context learning (ICL) as supervised learning, but recent work reported that the input-label correspondence is significantly less important than previously thought. Intrigued by this counter-intuitive observation, we re-examine the importance of ground-truth labels in in-context learning. With the introduction of two novel metrics, namely Label-Correctness Sensitivity and Ground-truth Label Effect Ratio (GLER), we were able to conduct quantifiable analysis on the impact of ground-truth label demonstrations. Through extensive analyses, we find that the correct input-label mappings can have varying impacts on the downstream in-context learning performances, depending on the experimental configuration. Through additional studies, we identify key components, such as the verbosity of prompt templates and the language model size, as the controlling factor to achieve more noise-resilient ICL.
%R 10.18653/v1/2022.emnlp-main.155
%U https://aclanthology.org/2022.emnlp-main.155
%U https://doi.org/10.18653/v1/2022.emnlp-main.155
%P 2422-2437
Markdown (Informal)
[Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations](https://aclanthology.org/2022.emnlp-main.155) (Yoo et al., EMNLP 2022)
ACL
- Kang Min Yoo, Junyeob Kim, Hyuhng Joon Kim, Hyunsoo Cho, Hwiyeol Jo, Sang-Woo Lee, Sang-goo Lee, and Taeuk Kim. 2022. Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 2422–2437, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.