1 citations · 1 across the 6 of their papers we have counts for
6 papers
Institutional Books - Enriched Text: A customizable multilingual open-source pipeline for denoising, deduplicating, and annotating OCR text at scale
David Lowry-Duda, Matteo Cargnelutti, Catherine Brobston +4
Released in 2025, Institutional Books: Harvard Library (IB-HL) is a collection of 983,004 volumes (242B o200k_base tokens), originally digitized through Harvard Library's participa…
Institutional Newspapers Pipeline: Deriving billions of high quality tokens from historical newspapers
Matteo Cargnelutti, Catherine Brobston, Eben English +6
Historical newspapers are an abundant record of public life, but their dense, irregular and sometimes noisy layouts make computational access to these materials both challenging an…
Institutional Books - Visual Elements: An open-source pipeline for extracting, classifying, deduplicating, and captioning visual elements from digital book collections
Jimmy Mendez, Matteo Cargnelutti, David Lowry-Duda +5
Historical book collections contain rich visual elements - such as illustrations, photographs, engravings, and decorative art - that are frequently under-explored in large-scale di…
GRIN Transfer: A production-ready tool for libraries to retrieve digital copies from Google Books
Liza Daly, Matteo Cargnelutti, Catherine Brobston +4
Publicly launched in 2004, the Google Books project has scanned tens of millions of items in partnership with libraries around the world. As part of this project, Google created th…
Institutional Books 1.0: A 242B token dataset from Harvard Library's collections, refined for accuracy and usability
Matteo Cargnelutti, Catherine Brobston, John Hess +8
Large language models (LLMs) use data to learn about the world in order to produce meaningful correlations and predictions. As such, the nature, scale, quality, and diversity of th…
SEAL: Systematic Error Analysis for Value ALignment
Manon Revel, Matteo Cargnelutti, Tyna Eloundou +1
Reinforcement Learning from Human Feedback (RLHF) aims to align language models (LMs) with human values by training reward models (RMs) on binary preferences and using these RMs to…