Hybrid Legal Reasoning Approaches for COLIEE 2025

The COLIEE competition for legal text processing includes four challenge tasks—case law retrieval (Task 1), case law entailment (Task 2), statute law retrieval (Task 3), and statute law entailment (Task 4). These tasks are designed to address practical pressures in the legal information processing domain. In this paper, we present our approach to all four tasks: for case law retrieval, a hybrid method that combines traditional lexical techniques with large language models (LLMs); for case law entailment, a modular retrieval–inference pipeline that integrates lexical and dense retrieval with zero-shot and few-shot LLMs; for statute law retrieval, we cast the problem as a supervised learning task that guides the fine-tuning of LLMs; and for statute law entailment, we employ a domain-specific LLM to identify similarities among instances of case descriptions. For each approach to each task, we summarize previous work and provide an evaluation of the value of our extensions for each task.

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Publication Details

Journal
The Review of Socionetwork Strategies
Published
2026-03-27
DOI
https://doi.org/10.1007/s12626-026-00208-x
Primary Topic
Artificial Intelligence in Law
Type
article
Field-Weighted Citation Impact
0.00
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article

Hybrid Legal Reasoning Approaches for COLIEE 2025

Housam Khalifa Bashier Babiker, Jiayi Dai, Yeji Kim, Euijin Baek et al.
The Review of Socionetwork Strategies
Artificial Intelligence in Law
article

Hybrid Legal Reasoning Approaches for COLIEE 2025

Housam Khalifa Bashier Babiker, Jiayi Dai, Yeji Kim, Euijin Baek, Mi-Young Kim, Abhita Gupta, Randy Goebel, H. M. Quamran Hasan
article en

Abstract

The COLIEE competition for legal text processing includes four challenge tasks—case law retrieval (Task 1), case law entailment (Task 2), statute law retrieval (Task 3), and statute law entailment (Task 4). These tasks are designed to address practical pressures in the legal information processing domain. In this paper, we present our approach to all four tasks: for case law retrieval, a hybrid method that combines traditional lexical techniques with large language models (LLMs); for case law entailment, a modular retrieval–inference pipeline that integrates lexical and dense retrieval with zero-shot and few-shot LLMs; for statute law retrieval, we cast the problem as a supervised learning task that guides the fine-tuning of LLMs; and for statute law entailment, we employ a domain-specific LLM to identify similarities among instances of case descriptions. For each approach to each task, we summarize previous work and provide an evaluation of the value of our extensions for each task.

The Review of Socionetwork Strategies
University of Alberta (CA)
Peace, Justice and strong institutions
Openalex Percentile: Top 7%
Artificial Intelligence in Law
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