2 citations · 6 across the 5 of their papers we have counts for
5 papers · 1 filter
pdfQA: Diverse, Challenging, and Realistic Question Answering over PDFs
Tobias Schimanski, Imene Kolli, Yu Fan +4
PDFs are the second-most used document type on the internet (after HTML). Yet, existing QA datasets commonly start from text sources or only address specific domains. In this paper…
Towards Faithful and Robust LLM Specialists for Evidence-Based Question-Answering
Tobias Schimanski, Jingwei Ni, Mathias Kraus +2
Advances towards more faithful and traceable answers of Large Language Models (LLMs) are crucial for various research and practical endeavors. One avenue in reaching this goal is b…
Automated Fact-Checking of Climate Change Claims with Large Language Models
Markus Leippold, Saeid Ashraf Vaghefi, Dominik Stammbach +10
This paper presents Climinator, a novel AI-based tool designed to automate the fact-checking of climate change claims. Utilizing an array of Large Language Models (LLMs) informed b…
Exploring Nature: Datasets and Models for Analyzing Nature-Related Disclosures
Tobias Schimanski, Chiara Colesanti Senni, Glen Gostlow +3
Nature is an amorphous concept. Yet, it is essential for the planet's well-being to understand how the economy interacts with it. To address the growing demand for information on c…
CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools
Jingwei Ni, Julia Bingler, Chiara Colesanti-Senni +10
In the face of climate change, are companies really taking substantial steps toward more sustainable operations? A comprehensive answer lies in the dense, information-rich landscap…