NOWJ@COLIEE 2026: Adaptive Pipelines for Legal Retrieval and Reasoning

NOWJ@COLIEE 2026：用于法律检索与推理的自适应流水线

Abstract: This paper presents the methodologies and results of the NOWJ team’s participation across all five tasks of the COLIEE 2026 competition. For Task 1 (Legal Case Retrieval), we propose a four-stage pipeline comprising candidate filtering, dense retrieval with complementary embedding models, cross-encoder reranking via fine-tuned generative rerankers and MLP-based pairwise classification, and adaptive per-query cutoff prediction.

摘要： 本文介绍了 NOWJ 团队参与 COLIEE 2026 竞赛全部五项任务的方法论及成果。针对任务 1（法律案例检索），我们提出了一套四阶段流水线，包括候选集过滤、结合互补嵌入模型的稠密检索、通过微调生成式重排序器与基于 MLP 的成对分类进行的交叉编码器重排序，以及针对每个查询的自适应截断预测。

For Task 2 (Legal Case Entailment), we combine BM25 filtering, T5-based reranking, and LLM-based entailment verification with consensus ensemble. For Task 3 (Statute Law Retrieval and Entailment), we adopt a retrieval-augmented generation framework with dense retrieval, attention-based reranking, and few-shot-prompted LLM reasoning.

针对任务 2（法律案例蕴含），我们结合了 BM25 过滤、基于 T5 的重排序以及基于大语言模型（LLM）的蕴含验证与共识集成。针对任务 3（成文法检索与蕴含），我们采用了检索增强生成（RAG）框架，结合了稠密检索、基于注意力机制的重排序以及少样本提示的大语言模型推理。

For Task 4 (Legal Textual Entailment), we introduce a dynamic routing pipeline that classifies query difficulty and dispatches cases to either a balanced few-shot solver or a structured zero-shot chain-of-thought solver. For the Pilot Task (Legal Judgment Prediction), we combine hierarchical transformers with CRF layers, argument relation mining, and probabilistic argumentation graph reasoning.

针对任务 4（法律文本蕴含），我们引入了一种动态路由流水线，通过对查询难度进行分类，将案例分发至平衡少样本求解器或结构化零样本思维链求解器。针对试点任务（法律判决预测），我们将分层 Transformer 与 CRF 层、论点关系挖掘以及概率论证图推理相结合。