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Beiduo Chen

Beiduo Chen has 4 papers in our library, in 2024–2026, most of them on Reasoning; often with Barbara Plank, Siyao Peng, Anna Korhonen.

4 papers in our library · 2024–2026

OpenAlex · h-index 2 · 12 works · 5 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 33 times in our library · ⭐ Paper Stars ranking: #8155

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

🎓Munich Center for Machine LearningMunich · Germany2024–2026 · 7 paperscurrent🎓University of CambridgeCambridge · United Kingdom2024–2026 · 5 paperscurrent🎓Ludwig-Maximilians-Universität MünchenMunich · Germany2025–2026 · 3 paperscurrent

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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 4 worksArts and Humanities · 4 worksComputer Science · 4 worksLanguage and Linguistics · 4 works

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 4 works

 Works by year

2020s

20261 paper20252 papers20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. “Seeing the Big through the Small”: Can LLMs Approximate Human Judgment Distributions on NLI from a Few Explanations? (2024 · 18 citations)
    When several annotators disagree on an NLI item for legitimate reasons, the spread of their labels is informative, and researchers capture it as a human judgment distribution (HJD).
  2. A Rose by Any Other Name: LLM-Generated Explanations Are Good Proxies for Human Explanations to Collect Label Distributions on NLI (2025 · 9 citations)
    Human annotators often disagree on natural language inference (NLI) items. That disagreement can be recorded as a human judgment distribution (HJD), but measuring it well takes many annotators per item. Earlier work showed that LLMs can estimate HJDs from a few human labels paired with human-written explanations.…
  3. LiTEx: A Linguistic Taxonomy of Explanations for Understanding Within-Label Variation in Natural Language Inference (2025 · 8 citations)
    Research on NLI disagreement usually studies annotators who choose different labels. This paper targets a less visible case: annotators who pick the same label but justify it in different ways. Highlighted spans, the usual structured form of explanation, cannot show this. Identical spans can hide different arguments…
  4. Agree, Disagree, Explain: Decomposing Human Label Variation in NLI through the Lens of Explanations (2026 · 3 citations)
    NLI datasets often contain conflicting labels for the same premise–hypothesis pair. Earlier work with the LiTEx taxonomy studied only one kind of variation: annotators who pick the same label but justify it differently. This paper extends LiTEx to cases where the labels themselves diverge. One trained annotator…

 Co-authors here

8 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 33 times (of the 149,439 papers whose bibliographies we read); 8 citing papers with a page here →.

Updated

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