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Conference Paper/Proceeding/Abstract 291 views 54 downloads

Can Large Language Models Reliably Extract Jurisdictional Variations? An Empirical Study on UK Statutory Texts

Livio Robaldo Orcid Logo, Safia Kanwal, Hafsa Dar, Davide Liga, kuuku Anim

Proc. of 22nd International Conference on Frontiers of Information Technology (FIT'25)

Swansea University Authors: Livio Robaldo Orcid Logo, Safia Kanwal, kuuku Anim

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Published in: Proc. of 22nd International Conference on Frontiers of Information Technology (FIT'25)
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URI: https://cronfa.swan.ac.uk/Record/cronfa70880
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spelling 2026-01-15T11:17:56.7570657 v2 70880 2025-11-12 Can Large Language Models Reliably Extract Jurisdictional Variations? An Empirical Study on UK Statutory Texts b711cf9f3a7821ec52bd1e53b4f6cf9e 0000-0003-4713-8990 Livio Robaldo Livio Robaldo true false c6c825948e8c21cf07dbff0709ab2ec6 Safia Kanwal Safia Kanwal true false bf3b60b3f57a5da2add996068307355e kuuku Anim kuuku Anim true false 2025-11-12 HRCL Conference Paper/Proceeding/Abstract Proc. of 22nd International Conference on Frontiers of Information Technology (FIT'25) 0 0 0 0001-01-01 COLLEGE NANME Hillary Rodham Clinton Law School COLLEGE CODE HRCL Swansea University Not Required Innovate UK project 10106412: "Odyssey - Opening the National Archives Legal Data to AI for Access to Justice (A2J)" 2026-01-15T11:17:56.7570657 2025-11-12T08:21:27.7613053 Faculty of Humanities and Social Sciences Hilary Rodham Clinton School of Law Livio Robaldo 0000-0003-4713-8990 1 Safia Kanwal 2 Hafsa Dar 3 Davide Liga 4 kuuku Anim 5 70880__35602__d30e1a13a07e43a390c2d1d6ffb07d8b.pdf paper-117.pdf 2025-11-12T08:23:31.3206426 Output 217304 application/pdf Accepted Manuscript true Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy (rights retention). true eng https://creativecommons.org/licenses/by/4.0/deed.en
title Can Large Language Models Reliably Extract Jurisdictional Variations? An Empirical Study on UK Statutory Texts
spellingShingle Can Large Language Models Reliably Extract Jurisdictional Variations? An Empirical Study on UK Statutory Texts
Livio Robaldo
Safia Kanwal
kuuku Anim
title_short Can Large Language Models Reliably Extract Jurisdictional Variations? An Empirical Study on UK Statutory Texts
title_full Can Large Language Models Reliably Extract Jurisdictional Variations? An Empirical Study on UK Statutory Texts
title_fullStr Can Large Language Models Reliably Extract Jurisdictional Variations? An Empirical Study on UK Statutory Texts
title_full_unstemmed Can Large Language Models Reliably Extract Jurisdictional Variations? An Empirical Study on UK Statutory Texts
title_sort Can Large Language Models Reliably Extract Jurisdictional Variations? An Empirical Study on UK Statutory Texts
author_id_str_mv b711cf9f3a7821ec52bd1e53b4f6cf9e
c6c825948e8c21cf07dbff0709ab2ec6
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author_id_fullname_str_mv b711cf9f3a7821ec52bd1e53b4f6cf9e_***_Livio Robaldo
c6c825948e8c21cf07dbff0709ab2ec6_***_Safia Kanwal
bf3b60b3f57a5da2add996068307355e_***_kuuku Anim
author Livio Robaldo
Safia Kanwal
kuuku Anim
author2 Livio Robaldo
Safia Kanwal
Hafsa Dar
Davide Liga
kuuku Anim
format Conference Paper/Proceeding/Abstract
container_title Proc. of 22nd International Conference on Frontiers of Information Technology (FIT'25)
institution Swansea University
college_str Faculty of Humanities and Social Sciences
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hierarchy_top_title Faculty of Humanities and Social Sciences
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hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str Hilary Rodham Clinton School of Law{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}Hilary Rodham Clinton School of Law
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active_str 0
published_date 0001-01-01T05:34:00Z
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