Contradiction Detection in Brazilian Legislative Bills: A Limited Labeled Data Study - Costa, Lucas G. L.; Souza, Átila M.; Andrade, Marcelo M.; Santos, Rodrygo L. T.; Meira Jr., Wagner; Pappa, Gisele L. (Anais do Brazilian e-Science Workshop (BreSci), 2026)
Identifying contradictory legislative bills is essential for ensuring consistency in Brazilian legal frameworks, yet it remains a manual and hard-to-scale task. In this work, we study automatic contradiction detection under limited labeled data. We use a dataset of 106 contradictory bill pairs curated by domain specialists and investigate two strategies: (i) supervised learning with synthetic data generation and (ii) zero-shot and few-shot classification using generative large language models (LLMs). Results show that few-shot LLMs perform strongly and outperform the supervised approach based on synthetic data. Although the small dataset limits generalization, our findings highlight the challenges of synthetic data in legal domains and the potential of LLMs for low-data scenarios.