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Siyuan Song

Siyuan Song has 3 papers in our library, in 2024–2026, most of them on Language models & pretraining, Implicature; often with Hai Hu, Jennifer Hu, Kanishka Misra.

3 papers in our library · 2024–2026

OpenAlex · h-index 2 · 13 works · 14 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 18 times in our library · ⭐ Paper Stars ranking: #8570

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

🎓The University of Texas at AustinAustin · United States2026 · 3 paperscurrent🎓Zhejiang UniversityHangzhou · China2025–2026 · 3 paperscurrent🎓Shanghai Jiao Tong UniversityShanghai · China2024–2026 · 2 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 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 worksExperimental and Cognitive Psychology · 1 workPsychology · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3 worksImplicature · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Implicature · 1 work

 Works by year

2020s

20261 paper20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Do Large Language Models Understand Conversational Implicature- A case study with a Chinese sitcom (2024 · 19 citations)
    The authors test whether large language models (LLMs) understand conversational implicature, that is, the indirect, non-literal meaning of an utterance, in Chinese.
  2. Language Models Fail to Introspect About Their Knowledge of Language (2025 · 9 citations)
    This work tests whether large language models can introspect, meaning whether they can accurately report on their own linguistic knowledge without fine-tuning.
  3. Bears, all bears, and some bears. Language Constraints on Language Models' Inductive Inferences (2026)
    The paper asks whether vision-language models (VLMs) treat three kinds of statements differently when generalizing a new property to an individual: universal statements ("all bears"), bare-plural generics ("bears"), and indefinite plurals ("some bears").

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

Updated

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