Argumentation and Judgement Factors: LLM-based Discovery and Application in Insurance Disputes.
| Authors | Basit Ali et al. |
| Year | 2026 |
| Venue | EACL 2026 |
| Paper | View on ACL Anthology |
| PDF | Download |
Abstract
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Engineering Breakdown
Plain English
This paper develops an LLM-based system to automatically discover and extract argumentation factors that influence judicial judgments in insurance dispute cases. The researchers train models to identify which legal arguments and evidence patterns actually correlate with case outcomes, then apply these patterns to predict and analyze new disputes.
Key Engineering Insight
The core technical contribution is moving from static legal rule extraction to learning empirical argumentation patterns directly from case outcomes—essentially training LLMs to reverse-engineer what factors judges actually weigh, rather than coding predefined legal rules.
Why It Matters for Engineers
Insurance and legal tech companies building case prediction or decision support systems need to move beyond keyword matching and precedent lookup. This work shows how to train models on historical outcomes to surface the implicit decision factors that matter, which directly improves accuracy of dispute resolution automation and reduces manual legal review overhead.
Research Context
Prior legal AI work focused on document classification and rule-based systems. This paper advances the field by combining LLMs with outcome analysis to discover latent argumentation patterns—bridging the gap between unstructured legal text and predictive modeling, which enables better generalization to new cases the system hasn't seen before.
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