Elamur Research⌕
☰
home / authors / Nils Dycke

Nils Dycke

Nils Dycke has 4 papers in our library, in 2023–2026, most of them on Dialogue systems, Low-resource languages, Reasoning; often with Iryna Gurevych, Ilia Kuznetsov, Anne Lauscher.

4 papers in our library · 2023–2026

OpenAlex · h-index 4 · 26 works · 57 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 85 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #7119

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

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. NLPeer: A Unified Resource for the Computational Study of Peer Review (2023 · 64 citations)
    Computational research on peer review has been held back by three gaps. Most available review data comes from machine learning conferences on OpenReview.net and carries no explicit license. Studies rarely look beyond one research community. Each dataset also uses its own task-specific format. NLPeer addresses all…
  2. Overview of PragTag-2023: Low-Resource Multi-Domain Pragmatic Tagging of Peer Reviews (2023 · 10 citations)
    Most NLP work on peer review has used reviews from machine learning conferences. This raises two concerns: models may fail when applied to other research fields, and labeled review data is scarce and costly to produce. The PragTag-2023 shared task, held at the 10th Workshop on Argument Mining, tested both problems…
  3. Automatic Reviewers Fail to Detect Faulty Reasoning in Research Papers: A New Counterfactual Evaluation Framework (2026 · 5 citations)
    LLMs are increasingly used as automatic review generators (ARGs), but studies of their quality disagree. One reason is that these evaluations mix many reviewing skills and often ignore publication context. Dycke and Gurevych isolate one skill that depends only on the paper itself: checking whether results…
  4. Decision-Making with Deliberation: Meta-reviewing as a Document-grounded Dialogue (2026 · 2 citations)
    The paper reframes meta-reviewing as a decision-making task rather than a summarization task. In this view, a dialogue agent answers the meta-reviewer's questions about the reviews. It can contrast reviewer opinions, help weigh arguments and place them in context, but it never volunteers verdicts. The authors set two…

All works

5 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

  1. What Can Natural Language Processing Do for Peer Review? ↗arXiv:2405.06563 · no summary yetIlia Kuznetsov et al.2024 · 33 citations

 Co-authors here

4 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 85 times (of the 149,439 papers whose bibliographies we read); 18 citing papers with a page here →.

Updated

↑ back to top

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