🧑‍🔬 Terry Ruas

🏛️ Affiliations

🎓University of GöttingenGöttingen · Germany2021–2026 · 57 papersEmployment since 2022 · Postodoctoral researcher, Informatikcurrent🎓University of WuppertalWuppertal · Germany2019–2023 · 25 papersEmployment 2019–2022 · Postdoctoral Researcher, Data & Knowledge Engineering Group🎓University of Michigan–DearbornDearborn · United States2012–2020 · 10 papersEducation 2015–2019 · Ph.D. Computer and Information Science, CISEmployment 2015–2017 · Graduate Student Instructor, CISEmployment 2018–2019 · Graduate Student Instructor, CIS🏛️National Research Council CanadaOttawa · Canada2022–2023 · 4 papers🎓Universidade Federal do ABCSanto André · Brazil2007–2014 · 4 papersEducation 2007–2010 · BS. Computer Science, CMCCEducation 2007–2010 · BS. Science and Technology, CMCCEducation 2012–2013 · MS. information Engineering, CECS🎓University of MichiganAnn Arbor · United States2016–2020 · 3 papers🔬National Institute of InformaticsTokyo · Japan2018Employment 2018 · Ph.D. Research Internship, Aizawa's Laboratory

🏛️IBM BrasilSao Paulo · Brazil2009–2015Employment 2009–2010 · Intern Support, Technology Support ServicesEmployment 2010–2012 · IT Specialist/Product Service, Technology Support ServicesEmployment 2012–2014 · Software Support Coordinator, Technology Support ServicesEmployment 2014–2015 · Product Manager, Cost Branding & Delivery Service Planner, Technology Support Services

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. 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.
2. ALDEN: Reinforcement Learning for Active Navigation and Evidence Gathering in Long Documents (2026)
   Vision-language models (VLMs) read single text-rich pages well but struggle when an answer requires reasoning across long, multi-page documents.
3. Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling (2026)
   Inference-time techniques can raise LLM accuracy without retraining, but they are usually reported by accuracy alone, which hides large differences in compute use.

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. Neural Media Bias Detection Using Distant Supervision With BABE - Bias Annotations By Experts ↗Findings of the Association for Computational Linguistics: EMNLP 2021 · no summary yetTimo Spinde et al.2021 · 98 citations
2. 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
3. Identifying Machine-Paraphrased Plagiarism ↗arXiv:2103.11909 · iConference 2022 · no summary yetJan Philip Wahle et al.2021 · 51 citations
4. 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
5. Evaluating Document Representations for Content-based Legal Literature Recommendations ↗arXiv:2104.13841 · no summary yetMalte Ostendorff et al.2021 · 34 citations
6. 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
7. 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
8. Why Machines Cannot Learn Mathematics, Yet ↗arXiv:1905.08359 (2019 http://ceur-ws.org/Vol-2414/paper14.pdf) · no summary yetAndré Greiner-Petter et al.2019 · 12 citations
9. Paraphrase Detection: Human vs. Machine Content ↗arXiv:2303.13989 · no summary yetJonas Becker et al.2023 · 11 citations
10. 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
11. 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
12. ALDEN: Reinforcement Learning for Active Navigation and Evidence Gathering in Long DocumentsAnnual Meeting of the Association for Computational Linguistics (ACL)Tianyu Yang, Terry Ruas, Yijun Tian, et al.2026
13. Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time ScalingAnnual Meeting of the Association for Computational Linguistics (ACL)Florian Valentin Wunderlich, Lars Benedikt Kaesberg, Jan Philip Wahle, et al.2026

🤝 Co-authors here

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

• Bela Gipp ×3
• Jan Philip Wahle ×3
• Daniel Kurzawe
• Tianyu Yang
• Yijun Tian

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
• Annual Meeting of the Association for Computational Linguistics (ACL) ×2: ACL 2026 ×2
• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
• Findings of the Association for Computational Linguistics: EMNLP 2021
• Proceedings of the 31st International Conference on Computational Linguistics

🔗 Cited by

Papers of our library cite their works 137 times (of the 149,439 papers whose bibliographies we read); 10 citing papers with a page here →.

Updated 2 November 2026

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