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Liam Dugan

Liam Dugan has 2 papers in our library, in 2024, most of them on Evaluation & benchmarks, Language models & pretraining, Question answering; often with Alyssa Hwang, Andrew Zhu, Chris Callison-Burch.

2 papers in our library · 2024

OpenAlex · ORCID · h-index 9 · 36 works · 790 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 1,325 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6068

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓University of PennsylvaniaPhiladelphia · United States2022–2026 · 25 paperscurrent🎓Carnegie Mellon UniversityPittsburgh · United States2021–2022 · 3 papersEmployment · Student, Chemistry🎓University College LondonLondon · United Kingdom2024 · 2 papers

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.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 3Research subjects and languages the work is about.Evaluation & benchmarks · 2 worksLanguage models & pretraining · 1 workQuestion answering · 1 work

 Works by year

2020s

20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors (2024 · 110 citations)
    Vendors and researchers often claim near-perfect accuracy for detecting AI-written text. These claims are rarely checked on common data, and existing benchmarks are too easy: they seldom vary decoding settings or include adversarial manipulation. The authors address this with RAID, a benchmark of about 6.2 million…
  2. FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark for Large Language Models (2024 · 51 citations)
    Many real-world requests are "fan-out" questions. To answer one, a system must first identify a set of entities and then gather and combine facts about each from separate documents. Existing benchmarks such as HotpotQA, LongBench and ZeroSCROLLS mostly test reasoning within one document or across only a few. FanOutQA…

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text DetectorsAnnual Meeting of the Association for Computational Linguistics (ACL)Liam Dugan, Alyssa Hwang, Filip Trhlík, et al.2024 · 110 citations
  2. FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark for Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Andrew Zhu, Alyssa Hwang, Liam Dugan, et al.2024 · 51 citations
  3. Interpretable-by-Design Text Understanding with Iteratively Generated Concept Bottleneck ↗arXiv (Cornell University) · no summary yetJosh Magnus Ludan et al.2023 · 12 citations

 Co-authors here

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

Venues

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

 Cited by

Papers of our library cite their works 1,325 times (of the 149,439 papers whose bibliographies we read); 323 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/liam-dugan/