@inproceedings{ali-etal-2026-argumentation,
title = "Argumentation and Judgement Factors: {LLM}-based Discovery and Application in Insurance Disputes",
author = "Ali, Basit and
Sinha, Anubhav and
Ramrakhiyani, Nitin and
Pawar, Sachin and
Palshikar, Girish Keshav and
Apte, Manoj",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-long.128/",
pages = "2789--2804",
ISBN = "979-8-89176-380-7",
abstract = "In this work, we focus on discovery of legal factors for a specific case type under consideration (e.g., vehicle insurance disputes). We refer to these legal factors more explicitly as ``Argumentation and Judgement Factors'' (AJFs). AJFs encode specific legal knowledge that is important for legal argumentation and judicial decision making. We propose a multi-step approach for discovering a list of AJFs for a given case type using a set of relevant legal documents (e.g., past judgements, relevant acts) and Symbolic Knowledge Distillation (SKD) from a Large Language Model (LLM). We propose a novel geneRatE-CRitic-reviEW (RECREW) prompting strategy for effective SKD. We construct and evaluate the discovered list of AJFs on two different types of cases (auto-insurance and life insurance) and show their utility in a dispute resolution application."
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<abstract>In this work, we focus on discovery of legal factors for a specific case type under consideration (e.g., vehicle insurance disputes). We refer to these legal factors more explicitly as “Argumentation and Judgement Factors” (AJFs). AJFs encode specific legal knowledge that is important for legal argumentation and judicial decision making. We propose a multi-step approach for discovering a list of AJFs for a given case type using a set of relevant legal documents (e.g., past judgements, relevant acts) and Symbolic Knowledge Distillation (SKD) from a Large Language Model (LLM). We propose a novel geneRatE-CRitic-reviEW (RECREW) prompting strategy for effective SKD. We construct and evaluate the discovered list of AJFs on two different types of cases (auto-insurance and life insurance) and show their utility in a dispute resolution application.</abstract>
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%0 Conference Proceedings
%T Argumentation and Judgement Factors: LLM-based Discovery and Application in Insurance Disputes
%A Ali, Basit
%A Sinha, Anubhav
%A Ramrakhiyani, Nitin
%A Pawar, Sachin
%A Palshikar, Girish Keshav
%A Apte, Manoj
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F ali-etal-2026-argumentation
%X In this work, we focus on discovery of legal factors for a specific case type under consideration (e.g., vehicle insurance disputes). We refer to these legal factors more explicitly as “Argumentation and Judgement Factors” (AJFs). AJFs encode specific legal knowledge that is important for legal argumentation and judicial decision making. We propose a multi-step approach for discovering a list of AJFs for a given case type using a set of relevant legal documents (e.g., past judgements, relevant acts) and Symbolic Knowledge Distillation (SKD) from a Large Language Model (LLM). We propose a novel geneRatE-CRitic-reviEW (RECREW) prompting strategy for effective SKD. We construct and evaluate the discovered list of AJFs on two different types of cases (auto-insurance and life insurance) and show their utility in a dispute resolution application.
%U https://aclanthology.org/2026.eacl-long.128/
%P 2789-2804
Markdown (Informal)
[Argumentation and Judgement Factors: LLM-based Discovery and Application in Insurance Disputes](https://aclanthology.org/2026.eacl-long.128/) (Ali et al., EACL 2026)
ACL