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Kai Shu

Kai Shu has 2 papers in our library, in 2020–2025, most of them on Text generation, Reasoning, Vision & language; often with Adaku Uchendu, Aman Rangapur, Carl Yang.

2 papers in our library · 2020–2025

OpenAlex · ORCID · Wikidata · h-index 42 · 187 works · 12,499 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 268 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #5657

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

🎓Emory UniversityAtlanta · United States2023–2026 · 27 papersEmployment since 2024current🎓Arizona State UniversityTempe · United States2015–2024 · 71 papersEducation 2015–2020 · PhD, Computer Science🎓Illinois Institute of TechnologyChicago · United States2020–2024 · 66 papersEmployment 2020–2024 · Assistant Professor, Computer Science🎓Chongqing UniversityChongqing · China2012–2014 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 8 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).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksSocial Sciences · 1 workComputer Vision and Pattern Recognition · 1 workSociology and Political Science · 1 work

🧭 Topics 3Research subjects and languages the work is about.Text generation · 1 workReasoning · 1 workVision & language · 1 work

 Works by year

2020s

20251 paper20201 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Authorship Attribution for Neural Text Generation (2020 · 196 citations)
    This EMNLP 2020 study treats the detection of neural text generators as an authorship problem. It asks three questions. First, do two texts come from the same source? Second, was a text produced by a person or by a machine? Third, which of k = 8 candidate generators produced a given text? The authors assembled a…
  2. Piecing It All Together: Verifying Multi-Hop Multimodal Claims (2025 · 8 citations)
    The paper addresses a gap in fact-checking research. Existing benchmarks either handle multimodal claims or multi-step textual reasoning, but none require both together. The authors define a new task, multi-hop multimodal claim verification. A system must combine several pieces of evidence from text, images and…

All works

6 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. Unsupervised Fake News Detection on Social Media: A Generative Approach ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetShuo Yang et al.2019 · 317 citations
  2. User Preference-aware Fake News Detection ↗International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · no summary yetYingtong Dou et al.2021 · 292 citations
  3. Combating Misinformation in the Age of LLMs: Opportunities and Challenges ↗arXiv:2311.05656 · no summary yetCanyu Chen, Kai Shu2023 · 214 citations
  4. Authorship Attribution for Neural Text GenerationConference on Empirical Methods in Natural Language Processing (EMNLP)Adaku Uchendu, Thai Le, Kai Shu, et al.2020 · 196 citations
  5. Can LLM-Generated Misinformation Be Detected? ↗arXiv:2309.13788 · no summary yetCanyu Chen, Kai Shu2023 · 108 citations
  6. Piecing It All Together: Verifying Multi-Hop Multimodal ClaimsInternational Conference on Computational Linguistics (COLING)Haoran Wang, Aman Rangapur, Xiongxiao Xu, et al.2025 · 8 citations

 Co-authors here

6 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 268 times (of the 149,439 papers whose bibliographies we read); 49 citing papers with a page here →.

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