🧑‍🔬 Jan Philip Wahle

🏛️ Affiliations

🎓University of GöttingenGöttingen · Germany2021–2026 · 56 papersEducation 2021–2025 · Ph.D., Computer ScienceEmployment 2022–2025 · Scientific Staff, Computer ScienceEmployment since 2025 · Research Fellow (Wissenschaftlicher Rat)current🎓University of WuppertalWuppertal · Germany2015–2026 · 11 papersEducation 2015–2018 · B.Sc. Information ScienceEducation 2018–2021 · M.Sc. Computer ScienceEmployment 2020–2025 · Laboratory Engineer, Computer Sciencecurrent🏛️National Research Council CanadaOttawa · Canada2022–2023 · 4 papersEmployment 2023 · Visiting Researcher, Digital Technologies (Advisor: Saif M. Mohammad)🏢AptivTroy · United States2018–2019Employment 2018–2019

🏛️Aptiv Germany WuppertalWuppertal · Germany2017–2019Employment 2017–2019 · Junior Software Developer, Artificial Intelligence (AI) Core

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Affect, Body, Cognition, Demographics, and Emotion: The ABCDE of Text Features for Computational Affective Science (2026 · 3 citations)
   Research in computational affective and social science often requires text that has already been tagged with features such as emotion-word usage or the author's age.
2. Who Watches the Watchmen? Humans Disagree With Translation Metrics on Unseen Domains (2026 · 2 citations)
   Most quality estimation (QE) metrics for machine translation are built and validated on WMT data, so it is unclear whether they remain reliable on new domains.
3. Age and Affect in Language: How Emotion Expression on Social Media Varies Across Adulthood (2026 · 1 citation)
   Psychology research disagrees on how emotional well-being changes over adulthood. Some studies report a U-shape, others an inverted U, and others no change. Most of this evidence comes from surveys, which are small and prone to bias, and it usually measures only valence. The authors offer a corpus-based alternative.…

All works

13 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

1. How Large Language Models are Transforming Machine-Paraphrase Plagiarism ↗Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · no summary yetJan Philip Wahle et al.2022 · 55 citations
2. Identifying Machine-Paraphrased Plagiarism ↗arXiv:2103.11909 · iConference 2022 · no summary yetJan Philip Wahle et al.2021 · 51 citations
3. Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection ↗arXiv:2103.12450 · JCDL 2021 · no summary yetJan Philip Wahle et al.2021 · 37 citations
4. Paraphrase Types for Generation and Detection ↗Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · no summary yetJan Philip Wahle, Bela Gipp, Terry Ruas2023 · 25 citations
5. Paraphrase Types Elicit Prompt Engineering Capabilities ↗Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · no summary yetJan Philip Wahle et al.2024 · 20 citations
6. Paraphrase Detection: Human vs. Machine Content ↗arXiv:2303.13989 · no summary yetJonas Becker et al.2023 · 11 citations
7. Towards Human Understanding of Paraphrase Types in Large Language Models ↗Proceedings of the 31st International Conference on Computational Linguistics · no summary yetDominik Meier et al.2025 · 4 citations
8. Affect, Body, Cognition, Demographics, and Emotion: The ABCDE of Text Features for Computational Affective ScienceComputational Affective Science (CAS) @ LRECJan Philip Wahle, Krishnapriya Vishnubhotla, Bela Gipp, et al.2026 · 3 citations
9. Who Watches the Watchmen? Humans Disagree With Translation Metrics on Unseen DomainsFindings of the Association for Computational LinguisticsFinn Schmidt, Jan Philip Wahle, Terry Ruas, et al.2026 · 2 citations
10. Age and Affect in Language: How Emotion Expression on Social Media Varies Across AdulthoodComputational Affective Science (CAS) @ LRECDaniela Teodorescu, Jan Philip Wahle, Saif M. Mohammad2026 · 1 citation
11. Language Modeling and Understanding Through Paraphrase Generation and Detection ↗arXiv:2602.08274 · no summary yetJan Philip Wahle2026
12. Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · no summary yetFlorian Valentin Wunderlich et al.2026
13. ALDEN: Reinforcement Learning for Active Navigation and Evidence Gathering in Long Documents ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetTianyu Yang et al.2026

🤝 Co-authors here

4 co-authors with a page, the most papers together first.

• Bela Gipp ×2
• Saif Mohammad ×2
• Daniela Teodorescu
• Krishnapriya Vishnubhotla

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing ×3
• Computational Affective Science (CAS) @ LREC ×2: Computational Affective Science (CAS) @ LREC 2026 ×2
• Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) ×2
• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
• Proceedings of the 31st International Conference on Computational Linguistics

🔗 Cited by

Papers of our library cite their works 96 times (of the 149,439 papers whose bibliographies we read); 9 citing papers with a page here →.

Updated 1 November 2026

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