Conference Paper/Proceeding/Abstract 328 views 83 downloads
Can Large Language Models Reliably Extract Jurisdictional Variations? An Empirical Study on UK Statutory Texts
2025 International Conference on Frontiers of Information Technology (FIT), Pages: 1 - 6
Swansea University Authors:
Safia Kanwal, Livio Robaldo , kuuku Anim
-
PDF | Accepted Manuscript
Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy (rights retention).
Download (212.21KB)
DOI (Published version): 10.1109/fit67061.2025.11333744
Abstract
Can Large Language Models Reliably Extract Jurisdictional Variations? An Empirical Study on UK Statutory Texts
| Published in: | 2025 International Conference on Frontiers of Information Technology (FIT) |
|---|---|
| ISBN: | 979-8-3315-7481-9 979-8-3315-7480-2 |
| ISSN: | 2334-3141 2473-7569 |
| Published: |
IEEE
2026
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa70880 |
| first_indexed |
2025-11-12T16:01:31Z |
|---|---|
| last_indexed |
2026-02-27T05:31:23Z |
| id |
cronfa70880 |
| recordtype |
SURis |
| fullrecord |
<?xml version="1.0"?><rfc1807><datestamp>2026-02-26T16:24:32.2413978</datestamp><bib-version>v2</bib-version><id>70880</id><entry>2025-11-12</entry><title>Can Large Language Models Reliably Extract Jurisdictional Variations? An Empirical Study on UK Statutory Texts</title><swanseaauthors><author><sid>c6c825948e8c21cf07dbff0709ab2ec6</sid><firstname>Safia</firstname><surname>Kanwal</surname><name>Safia Kanwal</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>b711cf9f3a7821ec52bd1e53b4f6cf9e</sid><ORCID>0000-0003-4713-8990</ORCID><firstname>Livio</firstname><surname>Robaldo</surname><name>Livio Robaldo</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>bf3b60b3f57a5da2add996068307355e</sid><firstname>kuuku</firstname><surname>Anim</surname><name>kuuku Anim</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2025-11-12</date><deptcode>HRCL</deptcode><abstract/><type>Conference Paper/Proceeding/Abstract</type><journal>2025 International Conference on Frontiers of Information Technology (FIT)</journal><volume>0</volume><journalNumber/><paginationStart>1</paginationStart><paginationEnd>6</paginationEnd><publisher>IEEE</publisher><placeOfPublication/><isbnPrint>979-8-3315-7481-9</isbnPrint><isbnElectronic>979-8-3315-7480-2</isbnElectronic><issnPrint>2334-3141</issnPrint><issnElectronic>2473-7569</issnElectronic><keywords/><publishedDay>13</publishedDay><publishedMonth>1</publishedMonth><publishedYear>2026</publishedYear><publishedDate>2026-01-13</publishedDate><doi>10.1109/fit67061.2025.11333744</doi><url/><notes/><college>COLLEGE NANME</college><department>Hillary Rodham Clinton Law School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>HRCL</DepartmentCode><institution>Swansea University</institution><apcterm>Not Required</apcterm><funders>Innovate UK project 10106412: "Odyssey - Opening the National Archives Legal Data to AI for Access to Justice (A2J)"</funders><projectreference/><lastEdited>2026-02-26T16:24:32.2413978</lastEdited><Created>2025-11-12T08:21:27.7613053</Created><path><level id="1">Faculty of Humanities and Social Sciences</level><level id="2">Hilary Rodham Clinton School of Law</level></path><authors><author><firstname>Safia</firstname><surname>Kanwal</surname><order>1</order></author><author><firstname>Livio</firstname><surname>Robaldo</surname><orcid>0000-0003-4713-8990</orcid><order>2</order></author><author><firstname>Hafsa</firstname><surname>Dar</surname><order>3</order></author><author><firstname>Davide</firstname><surname>Liga</surname><order>4</order></author><author><firstname>kuuku</firstname><surname>Anim</surname><order>5</order></author></authors><documents><document><filename>70880__35602__d30e1a13a07e43a390c2d1d6ffb07d8b.pdf</filename><originalFilename>paper-117.pdf</originalFilename><uploaded>2025-11-12T08:23:31.3206426</uploaded><type>Output</type><contentLength>217304</contentLength><contentType>application/pdf</contentType><version>Accepted Manuscript</version><cronfaStatus>true</cronfaStatus><documentNotes>Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy (rights retention).</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by/4.0/deed.en</licence></document></documents><OutputDurs/></rfc1807> |
| spelling |
2026-02-26T16:24:32.2413978 v2 70880 2025-11-12 Can Large Language Models Reliably Extract Jurisdictional Variations? An Empirical Study on UK Statutory Texts c6c825948e8c21cf07dbff0709ab2ec6 Safia Kanwal Safia Kanwal true false b711cf9f3a7821ec52bd1e53b4f6cf9e 0000-0003-4713-8990 Livio Robaldo Livio Robaldo true false bf3b60b3f57a5da2add996068307355e kuuku Anim kuuku Anim true false 2025-11-12 HRCL Conference Paper/Proceeding/Abstract 2025 International Conference on Frontiers of Information Technology (FIT) 0 1 6 IEEE 979-8-3315-7481-9 979-8-3315-7480-2 2334-3141 2473-7569 13 1 2026 2026-01-13 10.1109/fit67061.2025.11333744 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)" 2026-02-26T16:24:32.2413978 2025-11-12T08:21:27.7613053 Faculty of Humanities and Social Sciences Hilary Rodham Clinton School of Law Safia Kanwal 1 Livio Robaldo 0000-0003-4713-8990 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 Safia Kanwal Livio Robaldo 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 |
c6c825948e8c21cf07dbff0709ab2ec6 b711cf9f3a7821ec52bd1e53b4f6cf9e bf3b60b3f57a5da2add996068307355e |
| author_id_fullname_str_mv |
c6c825948e8c21cf07dbff0709ab2ec6_***_Safia Kanwal b711cf9f3a7821ec52bd1e53b4f6cf9e_***_Livio Robaldo bf3b60b3f57a5da2add996068307355e_***_kuuku Anim |
| author |
Safia Kanwal Livio Robaldo kuuku Anim |
| author2 |
Safia Kanwal Livio Robaldo Hafsa Dar Davide Liga kuuku Anim |
| format |
Conference Paper/Proceeding/Abstract |
| container_title |
2025 International Conference on Frontiers of Information Technology (FIT) |
| container_volume |
0 |
| container_start_page |
1 |
| publishDate |
2026 |
| institution |
Swansea University |
| isbn |
979-8-3315-7481-9 979-8-3315-7480-2 |
| issn |
2334-3141 2473-7569 |
| doi_str_mv |
10.1109/fit67061.2025.11333744 |
| publisher |
IEEE |
| college_str |
Faculty of Humanities and Social Sciences |
| hierarchytype |
|
| hierarchy_top_id |
facultyofhumanitiesandsocialsciences |
| hierarchy_top_title |
Faculty of Humanities and Social Sciences |
| hierarchy_parent_id |
facultyofhumanitiesandsocialsciences |
| 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 |
| document_store_str |
1 |
| active_str |
0 |
| published_date |
2026-01-13T05:31:12Z |
| _version_ |
1858708200550825984 |
| score |
11.453515 |

