The article "When Labels Say No, But Embeddings Say Yes" discusses how conventional retrieval metrics can overlook meaningful similarities between items. It highlights the limitations of relying solely on labels for evaluation and suggests that embeddings can capture nuanced relationships that labels miss. The piece underscores the importance of considering alternative metrics to better assess similarity in data retrieval tasks.
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.
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.
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...
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...
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.
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.
Prompted embedding models have recently received increasing attention, particularly for retrieval, where detailed retrieval instructions are provided as part of the retrieval prompt. Several new datas...
Enterprise Document Intelligence [Vol. 1 #7ter] - Six positions on the retrieval brick that contradict the cosine-first reflex of mainstream RAG The post The Untaught Lessons of RAG Retrieval: Cosine Is Not the Foundation appeared first on Towards Data Science .
Enterprise Document Intelligence [Vol. 1 #7A] - Stop searching strings.
Enterprise Document Intelligence [Vol. 1 #7B] - Retrieval is filtering on structured tables: keywords first, TOC second, embeddings last The post Finding the right anchors for RAG: keyword, embedding, and TOC signals in parallel appeared first on Towards Data Science .
The article "How Does a RAG Reranker Really Work?" explores the inner workings of Retrieval-Augmented Generation (RAG) rerankers, focusing on how data scientists explain the model’s operations behind the scenes. It discusses the impact of these insights on architecture decisions within enterprise document intelligence, specifically in the context of Enterprise Document Intelligence Vol.1 #2D. The piece highlights the importance of transparent model explanations for effective enterprise RAG implementation.
E‑SENS is a training‑free reranking technique designed to improve negative‑constraint retrieval in retrieval‑augmented language models. It works by extracting a compact ‘trap query’ that represents the excluded concept and subtracting the similarity of this trap query from the original retrieval score, thereby reducing the influence of documents that mention the excluded concept. Experiments on the ExcluIR benchmark demonstrate that E‑SENS achieves a clear recall‑violation trade‑off across four embedding models and effectively reduces trap retrieval while preserving recall.