Markus Leippold
Markus Leippold has 4 papers in our library, in 2020–2026, most of them on Reasoning, Discourse analysis, Language models & pretraining; often with Artem Shelmanov, Ekaterina Fadeeva, Janet Pierrehumbert.
OpenAlex · ORCID · Wikidata · h-index 36 · 253 works · 5,462 citations OpenAlex, 2026-10-14
📄 4 works in our library · 🔗 Cited 188 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6059
Affiliations
Fields, topics and concepts
📚 Shelves 12The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksComputer Science · 3 worksLanguage and Linguistics · 4 worksArtificial Intelligence · 3 worksSocial Sciences · 2 worksSociology and Political Science · 2 worksEconomics, Econometrics and Finance · 1 workFinance · 1 workInformation Systems · 1 workComputer Vision and Pattern Recognition · 1 workGeneral Social Sciences · 1 workPhilosophy · 1 work
🧭 Topics 5Research subjects and languages the work is about.Reasoning · 2 worksInformation retrieval & search · 1 workInterpretability · 1 workLanguage models & pretraining · 1 workDiscourse analysis · 1 work
Works by year
2020s
Papers
- CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims (2020 · 158 citations)
Automated fact-checking research has relied heavily on FEVER, whose claims were written by annotators and are short and simple. - AI for Climate Finance: Agentic Retrieval and Multi-Step Reasoning for Early Warning System Investments (2026 · 2 citations)
Climate-finance reports from Multilateral Development Banks (MDBs) and climate funds rarely label spending on Early Warning Systems (EWS) consistently. They arrive as messy PDFs with complex tables, which makes it hard to track progress toward the UN Early Warnings for All (EW4All) goal of universal coverage by 2027. - ReProbe: Efficient Test-Time Scaling of Multi-Step Reasoning by Probing Internal States of Large Language Models (2026 · 1 citation)
Test-time scaling improves LLM reasoning by sampling several candidate reasoning steps or full chains and keeping the most promising ones. - Actors, Frames and Arguments: A Multi-Decade Computational Analysis of Climate Discourse in Financial News using Large Language Models (2026)
The paper looks at how elite financial journalism has covered climate change over time. This area matters because such coverage can influence investment and policy, yet it has received far less study than general news. The authors built the Dow Jones Climate News Corpus from Dow Jones Newswire articles published…
All works
- CLIMATE-FEVER: A Dataset for Verification of Real-World Climate ClaimsarXiv:2012.00614Thomas Diggelmann, Jordan Boyd-Graber, Jannis Bulian, et al.2020 · 158 citations
- LEXam: Benchmarking Legal Reasoning on 340 Law Exams ↗arXiv:2505.12864 · no summary yetYu Fan et al.2025 · 50 citations
- AI for Climate Finance: Agentic Retrieval and Multi-Step Reasoning for Early Warning System InvestmentsConference of the European Chapter of the Association for Computational Linguistics (EACL)Ario Saeid Vaghefi, Aymane Hachcham, Veronica Grasso, et al.2026 · 2 citations
- ReProbe: Efficient Test-Time Scaling of Multi-Step Reasoning by Probing Internal States of Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Jingwei Ni, Ekaterina Fadeeva, Tianyi Wu, et al.2026 · 1 citation
- Actors, Frames and Arguments: A Multi-Decade Computational Analysis of Climate Discourse in Financial News using Large Language ModelsFindings of the Association for Computational LinguisticsRuiran Su, Markus Leippold, Janet B. Pierrehumbert2026
Co-authors here
- Artem Shelmanov
- Ekaterina Fadeeva
- Janet Pierrehumbert
- Jannis Bulian
- Jiaheng Zhang
- Jingwei Ni
- Jordan Boyd-Graber
- Massimiliano Ciaramita
- Mrinmaya Sachan
- Mubashara Akhtar
- See-Kiong Ng
- Tianyi Wu
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL): ACL 2026
- Conference of the European Chapter of the Association for Computational Linguistics (EACL): EACL 2026
- Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
Cited by
Updated
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