LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development

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In this work, we conduct a detailed analysis on the performance of legal-oriented pre-trained language models (PLMs). We examine the interplay between their original objective, acquired knowledge, and legal language understanding capacities which we define as the upstream, probing, and downstream performance, respectively. We consider not only the models' size but also the pre-training corpora used as important dimensions in our study. To this end, we release a multinational English legal corpus (LeXFiles) and a legal knowledge probing benchmark (LegalLAMA) to facilitate training and detailed analysis of legal-oriented PLMs. We release two new legal PLMs trained on LeXFiles and evaluate them alongside others on LegalLAMA and LexGLUE. We find that probing performance strongly correlates with upstream performance in related legal topics. On the other hand, downstream performance is mainly driven by the model's size and prior legal knowledge which can be estimated by upstream and probing performance. Based on these findings, we can conclude that both dimensions are important for those seeking the development of domain-specific PLMs.

TitelProceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Antal sider23
ForlagAssociation for Computational Linguistics (ACL)
ISBN (Elektronisk)9781959429722
StatusUdgivet - 2023
Begivenhed61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 - Toronto, Canada
Varighed: 9 jul. 202314 jul. 2023


Konference61st Annual Meeting of the Association for Computational Linguistics, ACL 2023
SponsorBloomberg Engineering, et al., Google Research, Liveperson, Meta, Microsoft

Bibliografisk note

Funding Information:
This work was partly funded by the In novation Fund Denmark (IFD, https: //innovationsfonden.dk/en) and the Fonds de recherche du Québec – Nature et technologies (FRQNT, https://frq.gouv.qc.ca/ nature-et-technologies/).

Publisher Copyright:
© 2023 Association for Computational Linguistics.

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