268 citations · 998 across the 27 of their papers we have counts for
30 papers
Identify, Locate, Link: End-to-End Key-Value Extraction from Document Images
A. Said Gurbuz, Ahmed Nassar, Christoph Auer +8
Document processing pipelines traditionally cascade optical character recognition (OCR) engines with downstream models for structured information extraction, leading to multi-stage…
Advanced Layout Analysis Models for Docling
Nikolaos Livathinos, Christoph Auer, Ahmed Nassar +16
This technical report documents the development of novel Layout Analysis models integrated into the Docling document-conversion pipeline. We trained several state-of-the-art object…
SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion
Ahmed Nassar, Andres Marafioti, Matteo Omenetti +10
We introduce SmolDocling, an ultra-compact vision-language model targeting end-to-end document conversion. Our model comprehensively processes entire pages by generating DocTags, a…
Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence
Granite Vision Team, Leonid Karlinsky, Assaf Arbelle +60
We introduce Granite Vision, a lightweight large language model with vision capabilities, specifically designed to excel in enterprise use cases, particularly in visual document un…
Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion
Nikolaos Livathinos, Christoph Auer, Maksym Lysak +14
We introduce Docling, an easy-to-use, self-contained, MIT-licensed, open-source toolkit for document conversion, that can parse several types of popular document formats into a uni…
Know Your RAG: Dataset Taxonomy and Generation Strategies for Evaluating RAG Systems
Rafael Teixeira de Lima, Shubham Gupta, Cesar Berrospi +4
Retrieval Augmented Generation (RAG) systems are a widespread application of Large Language Models (LLMs) in the industry. While many tools exist empowering developers to build the…
Data-Prep-Kit: getting your data ready for LLM application development
David Wood, Boris Lublinsky, Alexy Roytman +21
Data preparation is the first and a very important step towards any Large Language Model (LLM) development. This paper introduces an easy-to-use, extensible, and scale-flexible ope…
Docling Technical Report
Christoph Auer, Maksym Lysak, Ahmed Nassar +16
This technical report introduces Docling, an easy to use, self-contained, MIT-licensed open-source package for PDF document conversion. It is powered by state-of-the-art specialize…
Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIs
Lokesh Mishra, Sohayl Dhibi, Yusik Kim +4
Environment, Social, and Governance (ESG) KPIs assess an organization's performance on issues such as climate change, greenhouse gas emissions, water consumption, waste management,…
INDUS: Effective and Efficient Language Models for Scientific Applications
Bishwaranjan Bhattacharjee, Aashka Trivedi, Masayasu Muraoka +33
Large language models (LLMs) trained on general domain corpora showed remarkable results on natural language processing (NLP) tasks. However, previous research demonstrated LLMs tr…
ESG Accountability Made Easy: DocQA at Your Service
Lokesh Mishra, Cesar Berrospi, Kasper Dinkla +11
We present Deep Search DocQA. This application enables information extraction from documents via a question-answering conversational assistant. The system integrates several techno…
ICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents
Christoph Auer, Ahmed Nassar, Maksym Lysak +3
Transforming documents into machine-processable representations is a challenging task due to their complex structures and variability in formats. Recovering the layout structure an…
FETA: Towards Specializing Foundation Models for Expert Task Applications
Amit Alfassy, Assaf Arbelle, Oshri Halimi +10
Foundation Models (FMs) have demonstrated unprecedented capabilities including zero-shot learning, high fidelity data synthesis, and out of domain generalization. However, as we sh…
DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis
Birgit Pfitzmann, Christoph Auer, Michele Dolfi +2
Accurate document layout analysis is a key requirement for high-quality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayN…
Delivering Document Conversion as a Cloud Service with High Throughput and Responsiveness
Christoph Auer, Michele Dolfi, André Carvalho +2
Document understanding is a key business process in the data-driven economy since documents are central to knowledge discovery and business insights. Converting documents into a ma…
Robust PDF Document Conversion Using Recurrent Neural Networks
Nikolaos Livathinos, Cesar Berrospi, Maksym Lysak +7
The number of published PDF documents has increased exponentially in recent decades. There is a growing need to make their rich content discoverable to information retrieval tools.…
An Information Extraction and Knowledge Graph Platform for Accelerating Biochemical Discoveries
Matteo Manica, Christoph Auer, Valery Weber +9
Information extraction and data mining in biochemical literature is a daunting task that demands resource-intensive computation and appropriate means to scale knowledge ingestion.…
Understanding repulsively mediated superconductivity of correlated electrons via massively parallel DMRG
Adrian Kantian, Michele Dolfi, Matthias Troyer +1
The so-called minimal models of unconventional superconductivity are lattice models of interacting electrons derived from materials in which electron pairing arises from purely rep…
Corpus Conversion Service: A Machine Learning Platform to Ingest Documents at Scale
Peter W J Staar, Michele Dolfi, Christoph Auer +1
Over the past few decades, the amount of scientific articles and technical literature has increased exponentially in size. Consequently, there is a great need for systems that can…
Corpus Conversion Service: A machine learning platform to ingest documents at scale [Poster abstract]
Peter W J Staar, Michele Dolfi, Christoph Auer +1
Over the past few decades, the amount of scientific articles and technical literature has increased exponentially in size. Consequently, there is a great need for systems that can…
Density redistribution effects in fermionic optical lattices
Medha Soni, Michele Dolfi, Matthias Troyer
We simulate a one dimensional fermionic optical lattice to analyse heating due to non-adiabatic lattice loading. Our simulations reveal that, similar to the bosonic case, density r…
An Efficient Matrix Product Operator Representation of the Quantum-Chemical Hamiltonian
Sebastian Keller, Michele Dolfi, Matthias Troyer +1
We describe how to efficiently construct the quantum chemical Hamiltonian operator in matrix product form. We present its implementation as a density matrix renormalization group (…
Pair Correlations in Doped Hubbard Ladders
Michele Dolfi, Bela Bauer, Sebastian Keller +1
Hubbard ladders are an important stepping stone to the physics of the two-dimensional Hubbard model. While many of their properties are accessible to numerical and analytical techn…
Minimizing nonadiabaticities in optical-lattice loading
Michele Dolfi, Adrian Kantian, Bela Bauer +1
In the quest to reach lower temperatures of ultra-cold gases in optical lattice experiments, non-adiabaticites during lattice loading are one of the limiting factors that prevent t…