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Philipp Mondorf

Philipp Mondorf has 6 papers in our library, in 2024–2026, most of them on Language models & pretraining, Evaluation & benchmarks, Reasoning; often with Barbara Plank, Leonardo Bertolazzi, Raffaella Bernardi.

6 papers in our library · 2024–2026

OpenAlex · h-index 3 · 32 works · 61 citations OpenAlex, 2026-10-14

📄 6 works in our library · 🔗 Cited 184 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #5619

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

🎓Ludwig-Maximilians-Universität MünchenMunich · Germany2024–2026 · 16 paperscurrent🎓Munich Center for Machine LearningMunich · Germany2024–2026 · 11 paperscurrent🏥LMU KlinikumMunich · Germany2024–2026 · 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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 6 worksComputer Science · 6 worksArtificial Intelligence · 6 worksLanguage and Linguistics · 6 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 6 worksEvaluation & benchmarks · 1 workReasoning · 1 workInterpretability · 1 work

 Works by year

2020s

20262 papers20252 papers20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks (2024 · 141 citations)
    Increasingly, the outputs of NLP systems are evaluated not by humans but by large language models acting as "judges".
  2. Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models — A Survey (2024 · 53 citations)
    Large language models score well on reasoning benchmarks. Accuracy alone, however, shows little about how they reach their answers, and it remains disputed whether they truly reason or mostly replay patterns memorized from training data. Mondorf and Plank survey studies that examine the reasoning process rather than…
  3. Circuit Compositions: Exploring Modular Structures in Transformer-Based Language Models (2025 · 8 citations)
    Mechanistic interpretability usually locates circuits (minimal subgraphs that drive a model's behaviour) one task at a time.
  4. The Validation Gap: A Mechanistic Analysis of How Language Models Compute Arithmetic but Fail to Validate It (2025 · 6 citations)
    LLMs can often solve arithmetic yet frequently miss errors in a given solution. This paper asks why, from an internal-mechanism perspective. Using templates, the authors created math word problems in which two single-digit numbers are summed to yield a two-digit result. Each solution either contains an injected…
  5. If Probable, Then Acceptable? Understanding Conditional Acceptability Judgments in Large Language Models (2026)
    The paper asks whether large language models judge "If A, then B" statements the way people do. In humans, such judgments depend on two things: how likely B is once A is assumed, and whether A actually bears on B (supports it, undermines it, or has nothing to do with it). The authors reuse the human-rated materials…
  6. Language Models Learn Universal Representations of Numbers and Here's Why You Should Care (2026)
    The paper asks how consistently large language models encode numbers. Earlier studies found sine-like number embeddings but did not measure how closely they match across models, and they did not use natural-language contexts. The authors examine eight open checkpoints from the OLMo 2, Llama 3 and Phi 4 families. They…

All works

7 works of theirs in our library, the most cited first; 6 with a page here, the others link their original until we summarise them.

  1. LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation TasksarXiv:2406.18403Anna Bavaresco, Raffaella Bernardi, Leonardo Bertolazzi, et al.2024 · 141 citations
  2. Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models — A SurveyarXiv:2404.01869Philipp Mondorf, Barbara Plank2024 · 53 citations
  3. Circuit Compositions: Exploring Modular Structures in Transformer-Based Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Philipp Mondorf, Sondre Wold, Barbara Plank2025 · 8 citations
  4. The Validation Gap: A Mechanistic Analysis of How Language Models Compute Arithmetic but Fail to Validate ItConference on Empirical Methods in Natural Language Processing (EMNLP)Leonardo Bertolazzi, Philipp Mondorf, Barbara Plank, et al.2025 · 6 citations
  5. If Probable, Then Acceptable? Understanding Conditional Acceptability Judgments in Large Language ModelsConference of the European Chapter of the Association for Computational Linguistics (EACL)Jasmin Orth, Philipp Mondorf, Barbara Plank2026
  6. Language Models Learn Universal Representations of Numbers and Here's Why You Should CareAnnual Meeting of the Association for Computational Linguistics (ACL)Michal Štefánik, Timothee Mickus, Marek Kadlčík, et al.2026
  7. Last Translation Benchmark ↗arXiv:2609.04173 · no summary yetVilem Zouhar et al.2026

 Co-authors here

12 of 15 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 184 times (of the 149,439 papers whose bibliographies we read); 47 citing papers with a page here →.

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