Poly-Vector Retrieval: Reference and Content Embeddings for Legal Documents

Retrieval-Augmented Generation (RAG) has emerged as an effective paradigm for generating contextually accurate answers by integrating Large Language Models (LLMs) with retrieval mechanisms. However, in legal contexts, users frequently reference norms by their labels or nicknames (e.g., Article 5 of the Constitution or Consumer Defense Code (CDC)), rather than by their content, posing challenges for traditional RAG approaches that rely solely on semantic embeddings of text. Furthermore, legal texts themselves heavily rely on explicit cross-references (e.g., "pursuant to Article 34") that function as pointers. Both scenarios pose challenges for traditional RAG approaches that rely solely on semantic embeddings of text, often failing to retrieve the necessary referenced content. This paper introduces Poly-Vector Retrieval, a method assigning multiple distinct embeddings to each legal provision: one embedding captures the content (the full text), another captures the label (the identifier or proper name), and optionally additional embeddings capture alternative denominations. Inspired by Frege's distinction between Sense and Reference, this poly-vector retrieval approach treats labels, identifiers and reference markers as rigid designators and content embeddings as carriers of semantic substance. Experiments on the Brazilian Federal Constitution demonstrate that Poly-Vector Retrieval significantly improves retrieval accuracy for label-centric queries and potential to resolve internal and external cross-references, without compromising performance on purely semantic queries. The study discusses philosophical and practical implications of explicitly separating reference from content in vector embeddings and proposes future research directions for applying this approach to broader legal datasets and other domains characterized by explicit reference identifiers.

Unlocking Legal Knowledge with Multi-Layered Embedding-Based Retrieval

This work addresses the challenge of capturing the complexities of legal knowledge by proposing a multi-layered embedding-based retrieval method for legal and legislative texts. Creating embeddings not only for individual articles but also for their components (paragraphs, clauses) and structural groupings (books, titles, chapters, etc), we seek to capture the subtleties of legal information through the use of dense vectors of embeddings, representing it at varying levels of granularity. Our method meets various information needs by allowing the Retrieval Augmented Generation system to provide accurate responses, whether for specific segments or entire sections, tailored to the user's query. We explore the concepts of aboutness, semantic chunking, and inherent hierarchy within legal texts, arguing that this method enhances the legal information retrieval. Despite the focus being on Brazil's legislative methods and the Brazilian Constitution, which follow a civil law tradition, our findings should in principle be applicable across different legal systems, including those adhering to common law traditions. Furthermore, the principles of the proposed method extend beyond the legal domain, offering valuable insights for organizing and retrieving information in any field characterized by information encoded in hierarchical text.