139 citations · 193 across the 14 of their papers we have counts for
5 papers · 1 filter
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…