38 citations · 148 across the 19 of their papers we have counts for
3 papers · 2 filters
AFaCTA: Assisting the Annotation of Factual Claim Detection with Reliable LLM Annotators
Jingwei Ni, Minjing Shi, Dominik Stammbach +3
With the rise of generative AI, automated fact-checking methods to combat misinformation are becoming more and more important. However, factual claim detection, the first step in a…
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…