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Chen Shani

Chen Shani has 2 papers in our library, in 2026, most of them on Language models & pretraining; often with Dafna Shahaf, Dan Jurafsky, Gabriel Stanovsky.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 5 · 42 works · 174 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 9 times in our library · 7 more in our library without a page yet · ⭐ Paper Stars ranking: #11922

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

🎓Stanford UniversityStanford · United States2025–2026 · 19 paperscurrent🎓Tel Aviv UniversityTel Aviv · Israel2026 · 4 papersEmployment since 2026 · Senior Lecturer, School of Industrial & Intelligent Systems Engineeringcurrent🎓Hebrew University of JerusalemJerusalem · Israel2020–2023 · 15 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksPsychology · 2 worksExperimental and Cognitive Psychology · 1 workSocial Psychology · 1 work

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. One Joke to Rule them All? On the (Im)possibility of Generalizing Humor Detection (2026)
    Computational humor research usually builds a separate model for each narrow genre, such as puns, memes or sarcasm.
  2. 🧑‍🍳 Cooking Up Creativity: Enhancing LLM Creativity through Structured Recombination (2026)
    LLMs write fluent text but tend to repeat familiar patterns when asked for original ideas. Raising temperature only adds token-level randomness and does not produce new concepts. The authors move the creative step outside the model. An LLM turns textual ideas into structured representations, an algorithm recombines…

All works

9 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. Labeling messages as AI-generated does not reduce their persuasive effects ↗PNAS Nexus · no summary yetIsabel O. Gallegos et al.2026 · 16 citations
  2. From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning ↗arXiv (Cornell University) · no summary yetChen Shani et al.2025 · 5 citations
  3. Rethinking Word Similarity: Semantic Similarity through Classification Confusion ↗Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · no summary yetKaitlyn Zhou et al.2025 · 4 citations
  4. The Roots of Performance Disparity in Multilingual Language Models: Intrinsic Modeling Difficulty or Design Choices? ↗arXiv (Cornell University) · no summary yetChen Shani et al.2026 · 2 citations
  5. Beyond Tokens: Concept-Level Training Objectives for LLMs ↗Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers) · no summary yetLaya Iyer et al.2026 · 1 citation
  6. One Joke to Rule them All? On the (Im)possibility of Generalizing Humor DetectionWorkshop on Computational HumorMor Turgeman, Chen Shani, Dafna Shahaf2026
  7. 🧑‍🍳 Cooking Up Creativity: Enhancing LLM Creativity through Structured RecombinationTransactions of the Association for Computational Linguistics (TACL)Moran Mizrahi, Chen Shani, Gabriel Stanovsky, et al.2026
  8. Categorize Early, Integrate Late: Divergent Processing Strategies in Automatic Speech Recognition ↗arXiv (Cornell University) · no summary yetNathan Roll et al.2026
  9. Learning Concepts, Not Tokens: Self-Supervised Semantic Alignment for Language Models ↗arXiv (Cornell University) · no summary yetChristine Zhang, Dan Jurafsky, Chen Shani2026

 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 9 times (of the 149,439 papers whose bibliographies we read); 2 citing papers with a page here →.

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