Methods for Estimating and Improving Robustness of Language
Models.

D 2022

Methods for Estimating and Improving Robustness of Language Models.

ŠTEFÁNIK, Michal

Basic information

Original name

Methods for Estimating and Improving Robustness of Language Models.

Authors

ŠTEFÁNIK, Michal (703 Slovakia, guarantor, belonging to the institution)

Edition

Seattle, Washington + Online, Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop, p. 44-51, 8 pp. 2022

Publisher

Association for Computational Linguistics

Other information

Language

English

Type of outcome

Proceedings paper

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

is not subject to a state or trade secret

Publication form

electronic version available online

References:

URL

RIV identification code

RIV/00216224:14330/22:00126309

Organization unit

Faculty of Informatics

ISBN

978-1-7138-5621-4

DOI

https://doi.org/10.18653/v1/2022.naacl-srw.6

UT WoS

000860760300006

EID Scopus

2-s2.0-85137539766

Keywords in English

natural language processing; transformers; robustness; generalization

Abstract

In the original language

Despite their outstanding performance, large language models (LLMs) suffer notorious flaws related to their preference for shallow textual relations over full semantic complexity of the problem. This proposal investigates a common denominator of this problem in their weak ability to generalise outside of the training domain. We survey diverse research directions providing estimations of model generalisation ability and find that incorporating some of these measures in the training objectives leads to enhanced distributional robustness of neural models. Based on these findings, we present future research directions enhancing the robustness of LLMs.

Links

MUNI/A/1195/2021, interní kód MU

Name: Aplikovaný výzkum v oblastech vyhledávání, analýz a vizualizací rozsáhlých dat, zpracování přirozeného jazyka a aplikované umělé inteligence

Investor: Masaryk University

Cite

ŠTEFÁNIK, Michal. Methods for Estimating and Improving Robustness of Language Models. Online. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop. Seattle, Washington + Online: Association for Computational Linguistics, 2022, p. 44-51. ISBN 978-1-7138-5621-4. Available from: https://doi.org/10.18653/v1/2022.naacl-srw.6.

@inproceedings{1875957,
   author = {Štefánik, Michal},
   address = {Seattle, Washington + Online},
   booktitle = {Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop},
   doi = {https://doi.org/10.18653/v1/2022.naacl-srw.6},
   keywords = {natural language processing; transformers; robustness; generalization},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Seattle, Washington + Online},
   isbn = {978-1-7138-5621-4},
   pages = {44-51},
   publisher = {Association for Computational Linguistics},
   title = {Methods for Estimating and Improving Robustness of Language Models.},
   url = {https://aclanthology.org/2022.naacl-srw.6/},
   year = {2022}
}

TY  - CONF
ID  - 1875957
AU  - Štefánik, Michal
PY  - 2022
TI  - Methods for Estimating and Improving Robustness of Language Models.
PB  - Association for Computational Linguistics
CY  - Seattle, Washington + Online
SN  - 9781713856214
KW  - natural language processing
KW  - transformers
KW  - robustness
KW  - generalization
UR  - https://aclanthology.org/2022.naacl-srw.6/
N2  - Despite their outstanding performance, large language models (LLMs) suffer notorious flaws related to their preference for shallow textual relations over full semantic complexity of the problem. This proposal investigates a common denominator of this problem in their weak ability to generalise outside of the training domain. We survey diverse research directions providing estimations of model generalisation ability and find that incorporating some of these measures in the training objectives leads to enhanced distributional robustness of neural models. Based on these findings, we present future research directions enhancing the robustness of LLMs.
ER  -

ŠTEFÁNIK, Michal. Methods for Estimating and Improving Robustness of Language Models. Online. In \textit{Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop}. Seattle, Washington + Online: Association for Computational Linguistics, 2022, p.~44-51. ISBN~978-1-7138-5621-4. Available from: https://doi.org/10.18653/v1/2022.naacl-srw.6.

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