Tobias Schimanski

Hello, dear visitor of my homepage :)

I am a PhD student at the Department of Finance at the University of Zurich, supervised by Prof. Markus Leippold and Prof. Zacharias Sautner. During my PhD, I held research positions at ETH Zurich with Prof. Elliott Ash and at the University of Oxford with Dr. Julia Bingler.

My research focuses on Natural Language Processing (NLP) for Sustainable Finance. Hence, I conduct research on NLP methods, specifically Large Language Models, and translate these to sustainable finance use cases in climate reporting, firm-level extreme weather event impacts, and adaptation & resilience.

My research vision is to establish an active community in NLP for Sustainable Finance, making the vast technological possibilities available to the pressing challenge of understanding, communicating, and tracking firms' climate change activities. Beyond academic papers, I organize workshops at leading computer science conferences, create and describe open-source tools & data (e.g., ESG-BERT has cumulated more than 1 million downloads), and disseminate knowledge (e.g., ClimateChangeAI tutorial).

Working Papers

Sustainable Finance

Tobias Schimanski (2026). Firm-level Climate Change Adaptation.

Tobias Schimanski, Glen Gostlow, Malte Toetzke, Markus Leippold (2026). What Firms Actually Lose (and Gain) from Extreme Weather Event Impacts.

Sebastian Gehricke, Markus Leippold, Tobias Schimanski, Cristhian Delgado Fajardo (2025). To Disclose, or Not to Disclose: Evaluating the Effectiveness of Mandatory Climate-Related Disclosure.

Natural Language Processing

Tobias Schimanski, Imene Kolli, Jingwei Ni, Yu Fan, Ario Saeid Vaghefi, Elliott Ash, Markus Leippold (2026). pdfQA: Diverse, Challenging, and Realistic Question Answering over PDFs. Under Review at Empirical Methods in Natural Language Processing (EMNLP).

Published Papers

Sustainable Finance

Chiara Colesanti Senni, Tobias Schimanski, Julia Bingler, Jingwei Ni, Markus Leippold (2025). Using AI to assess corporate climate transition disclosures. Environmental Research.

Tobias Schimanski, Andrin Reding, Nico Reding, Julia Bingler, Mathias Kraus, Markus Leippold (2024). Bridging the Gap in ESG Measurement: Using NLP to Quantify Environmental, Social, and Governance Communication. Finance Research Letters.

Natural Language Processing (“*” marks shared first author contribution)

Jingwei Ni*, Tobias Schimanski*, Meihong Lin, Mrinmaya Sachan, Elliott Ash, and Markus Leippold (2025). DIRAS: Efficient LLM Annotation of Document Relevance for Retrieval Augmented Generation. In: main conference of the Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL).

Tobias Schimanski*, Jingwei Ni*, Mathias Kraus, Elliott Ash, Markus Leippold (2024). Towards Faithful and Robust LLM Specialists for Evidence-Based Question-Answering. In: main conference of Association for Computational Linguistics (ACL).

Tobias Schimanski, Jingwei Ni, Roberto Spacey, Nicola Ranger, Markus Leippold (2024). ClimRetrieve: A Benchmarking Dataset for Information Retrieval from Corporate Climate Disclosures. In: main conference of Empirical Methods in Natural Language Processing (EMNLP).

Tobias Schimanski, Chiara Colesanti Senni, Glen Gostlow, Jingwei Ni, Tingyu Yu, Markus Leippold (2024). Exploring Nature: Datasets and Models for Analyzing Nature-Related Disclosures. In: Workshop on AI in Finance for Social Impact of Association for Advancement of Artificial Intelligence (AAAI).

Tobias Schimanski, Julia Bingler, Camilla Hyslop, Mathias Kraus, Markus Leippold (2023). ClimateBERT-NetZero: Detecting and Assessing Net Zero and Reduction Targets. In: main conference of Empirical Methods in Natural Language Processing (EMNLP).

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