Enhancing Legal LLMs through Metadata-Enriched RAG Pipelines and Direct Preference Optimization

ArXi:2603.19251v1 Announce Type: new Large Language Models (LLMs) perform well in short contexts but degrade on long legal documents, often producing hallucinations such as incorrect clauses or precedents. In the legal domain, where precision is critical, such errors undermine reliability and trust. Retrieval Augmented Generation (RAG) helps ground outputs but remains limited in legal settings, especially with small, locally deployed models required for data privacy.