To evaluate the quality of legal document embeddings, focus on three key areas: task performance, semantic relevance, and domain-specific accuracy. Legal embeddings are numerical representations of text designed to capture legal concepts, so their quality depends on how well they enable practical applications and reflect legal nuances. Start by testing embeddings in downstream tasks like document classification, retrieval, or summarization. For example, if your embeddings are used to classify case law into legal categories (e.g., “contract disputes” vs. “property rights”), measure metrics like precision, recall, or F1-score. If performance meets or exceeds baseline models (e.g., TF-IDF or simpler word embeddings), the embeddings are likely effective.
Next, assess semantic relevance using similarity metrics and clustering. Legal documents often rely on precise terminology, so embeddings should group related terms (e.g., “negligence” and “duty of care”) while distinguishing unrelated ones. Calculate cosine similarity between embeddings of known related concepts (e.g., “breach of contract” and “contract termination”) and compare them to unrelated pairs. Tools like UMAP or t-SNE can visualize embedding clusters to check if similar cases or statutes group logically. For instance, embeddings of employment law cases should cluster separately from tax law cases. If clusters align with legal categories, the embeddings capture meaningful structure.
Finally, validate domain-specific accuracy by testing on legal benchmarks or expert-reviewed datasets. Legal text contains jargon and context-dependent meanings (e.g., “consideration” in contract law versus everyday use). Use specialized datasets like COLIEE (legal case entailment) or LexGLUE (legal NLP tasks) to benchmark performance. For example, if your embeddings power a retrieval system, measure if they return relevant precedents for a query case. Incorporate human evaluation: have legal experts rate whether retrieved documents or embedding-based summaries align with their professional judgment. If embeddings perform well on these tests while handling ambiguities unique to law, they’re likely high quality. Combining automated metrics with domain expertise ensures robustness.