no code implementations • 16 Feb 2024 • Jingwei Ni, Minjing Shi, Dominik Stammbach, Mrinmaya Sachan, Elliott Ash, Markus Leippold
With the rise of generative AI, automated fact-checking methods to combat misinformation are becoming more and more important.
no code implementations • 13 Feb 2024 • Tobias Schimanski, Jingwei Ni, Mathias Kraus, Elliott Ash, Markus Leippold
One avenue in reaching this goal is basing the answers on reliable sources.
no code implementations • 23 Jan 2024 • Markus Leippold, Saeid Ashraf Vaghefi, Dominik Stammbach, Veruska Muccione, Julia Bingler, Jingwei Ni, Chiara Colesanti-Senni, Tobias Wekhof, Tobias Schimanski, Glen Gostlow, Tingyu Yu, Juerg Luterbacher, Christian Huggel
This paper presents Climinator, a novel AI-based tool designed to automate the fact-checking of climate change claims.
no code implementations • 28 Dec 2023 • Tobias Schimanski, Chiara Colesanti Senni, Glen Gostlow, Jingwei Ni, Tingyu Yu, Markus Leippold
Our approach is the first to respond to calls to assess corporate nature communication on a large scale.
1 code implementation • 28 Jul 2023 • Jingwei Ni, Julia Bingler, Chiara Colesanti-Senni, Mathias Kraus, Glen Gostlow, Tobias Schimanski, Dominik Stammbach, Saeid Ashraf Vaghefi, Qian Wang, Nicolas Webersinke, Tobias Wekhof, Tingyu Yu, Markus Leippold
In the face of climate change, are companies really taking substantial steps toward more sustainable operations?
no code implementations • 27 Jun 2023 • Jingwei Ni, Julia Bingler, Chiara Colesanti-Senni, Mathias Kraus, Glen Gostlow, Tobias Schimanski, Dominik Stammbach, Saeid Ashraf Vaghefi, Qian Wang, Nicolas Webersinke, Tobias Wekhof, Tingyu Yu, Markus Leippold
This paper introduces a novel approach to enhance Large Language Models (LLMs) with expert knowledge to automate the analysis of corporate sustainability reports by benchmarking them against the Task Force for Climate-Related Financial Disclosures (TCFD) recommendations.
2 code implementations • 23 May 2023 • Jingwei Ni, Zhijing Jin, Qian Wang, Mrinmaya Sachan, Markus Leippold
Due to the task difficulty and data scarcity in the Financial NLP domain, we explore when aggregating such diverse skills from multiple datasets with MTL can work.
no code implementations • 11 Apr 2023 • Saeid Ashraf Vaghefi, Qian Wang, Veruska Muccione, Jingwei Ni, Mathias Kraus, Julia Bingler, Tobias Schimanski, Chiara Colesanti-Senni, Nicolas Webersinke, Christrian Huggel, Markus Leippold
The answers and their sources were evaluated by our team of IPCC authors, who used their expert knowledge to score the accuracy of the answers from 1 (very-low) to 5 (very-high).
1 code implementation • NAACL 2022 • Jingwei Ni, Zhijing Jin, Markus Freitag, Mrinmaya Sachan, Bernhard Schölkopf
We show that these two factors have a large causal effect on the MT performance, in addition to the test-model direction mismatch highlighted by existing work on the impact of translationese.
1 code implementation • EMNLP 2021 • Zhijing Jin, Julius von Kügelgen, Jingwei Ni, Tejas Vaidhya, Ayush Kaushal, Mrinmaya Sachan, Bernhard Schölkopf
The principle of independent causal mechanisms (ICM) states that generative processes of real world data consist of independent modules which do not influence or inform each other.