Enterprise Document Intelligence [Vol. 1 #2] Why the same vector search that handles synonyms and paraphrase silently fails on negation, exact identifiers, and your company’s acronyms, and what to use when it does.
By angela shi
The article explores how word embeddings encode relationships between words within a learned vector space. It explains the mechanisms by which words become related through their positions and distances in that space. The post provides insights into the underlying processes that allow embeddings to capture semantic connections.
By Nikhil Dasari
I built four AI retrieval architectures on a laptop and benchmarked them against the same set of documents and questions. Here’s what the results taught me about the trade-offs between plain RAG, grap...
By Arijit Ghoshal
arXiv:2610.10508v1 Announce Type: new
Abstract: Prompted embedding models have recently received increasing attention, particularly for retrieval, where detailed retrieval instructions are provided a...
By Amanda Myntti, Jenna Kanerva, Veronika Laippala, Filip Ginter
The article explains that enterprise document intelligence can be categorized into three distinct corpus types, each requiring a specific architecture. It outlines how to determine the shape of a document collection through three key questions. The piece also discusses the costs associated with building a system for the incorrect corpus type.
By angela shi
The article explains how LoRA fine‑tuning addressed an under‑labeling issue in the SigLip model. It outlines that whether this approach is suitable depends on three specific questions. The post provides guidance on evaluating the appropriateness of fine‑tuning for your own use case.
By Miikka Silfverberg