Comparative Evaluation Report of Embedding Models for Korean Legal Documents
1. Evaluation Overview Objective: Select embedding models optimized for Korean statutes and ordinance search (RAG) systems Evaluation Dataset KCL-MCQA (Korean Canonical Legal Benchmark) 282 questions, 867 case law documents (expert-tagged Ground Truth) Rationale for Data Selection Currently, no public benchmark dataset exists for Korean statutes/ordinances KCL-MCQA is the only verified Korean search evaluation dataset in the legal domain Case law and statutes/ordinances share identical legal terminology and writing style, enabling similar embedding performance expectations Re-evaluation recommended when statute/ordinance-specific evaluation datasets are built Evaluation Environment ...