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Leonidas Gee

dblp:343/5898 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Trustworthy machine learning · 67% Efficient and distributed learning · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Efficient and distributed learning
model compression		 0.712023
Are Compressed Language Models Less Subgroup Robust? · EMNLP 2023
Machine learning › Trustworthy machine learning
robustness		 0.712023
Are Compressed Language Models Less Subgroup Robust? · EMNLP 2023
Machine learning › Trustworthy machine learning › robustness › distributional robustness
subgroup robustness		 0.712023
Are Compressed Language Models Less Subgroup Robust? · EMNLP 2023

Methods — techniques the papers use, named apart from their topics

worst-group performance analysis · 0.7BERT · 0.7
YearPublicationVenuePosition
2023 Are Compressed Language Models Less Subgroup Robust?
abstract
To reduce the inference cost of large language models, model compression is increasingly used to create smaller scalable models.However, little is known about their robustness to minority subgroups defined by the labels and attributes of a dataset.In this paper, we investigate the effects of 18 different compression methods and settings on the subgroup robustness of BERT language models.We show that worst-group performance does not depend on model size alone, but also on the compression method used.Additionally, we find that model compression does not always worsen the performance on minority subgroups.Altogether, our analysis serves to further research into the subgroup robustness of model compression.
Leonidas Gee, Andrea Zugarini, Novi Quadrianto
 EMNLP1