Poly-Vector Retrieval: Reference and Content Embeddings for Legal Documents · HackerPapers discussionshttps://hackerpaper.kaipfister.de/p/2504.10508/Collected HN comments and submissions linking this paper.enMon, 02 Nov 2026 11:16:22 GMTPoly-Vector Retrieval: Reference and Content Embeddings for Legal Documents — mentioned by joaoli131https://news.ycombinator.com/item?id=43967801urn:hackerpapers:mention:2504.10508:43967801Mon, 12 May 2025 21:49:28 GMT<p>Embeddings are still underrated—even in RAG. Legal text is deeply hierarchical and full of pointers (“Art. 5 CF”, “see Art 34”). One vector per article leaves too much on the table. Things that moved the needle for us: – *Multi-layer embeds* vectors for every paragraph and every structural level (chapter → book). Retriever picks the right granularity. (arXiv:2411.07739) – *Propositional queries* strip speech-act fluff (“could you please…”) before embedding. Similarity + top-k recall jump. (arXiv:2503.10654) – *Poly-vector retrieval* two vectors per norm—one for content, one for the…</p><p><a href="https://hackerpaper.kaipfister.de/p/2504.10508/">Paper and discussion history</a>. A mention is not an endorsement.</p>cs.IR