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Delong Chen

Delong Chen has 3 papers in our library, in 2022–2024, most of them on Language models & pretraining, Text generation, Bias, fairness & safety; often with Yejin Bang, Etsuko Ishii, Nayeon Lee.

3 papers in our library · 2022–2024

OpenAlex · ORCID · h-index 12 · 48 works · 971 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 1,301 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #547

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

🎓Hong Kong University of Science and TechnologyHong Kong · China2023–2026 · 23 papersEducation🎓Hohai UniversityNanjing · China2017–2023 · 16 papersEducation 2017–2021 · B.S.Employment since 2021 · Research Assistant
🏛️Megvii Technology LimitedBeijing · China2021Employment since 2021 · Research Intern

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 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 worksComputer Science · 3 worksArts and Humanities · 2 worksLanguage and Linguistics · 2 worksSafety Research · 1 workSocial Sciences · 1 work

🧭 Topics 3Research subjects and languages the work is about.Text generation · 1 workLanguage models & pretraining · 2 worksBias, fairness & safety · 1 work

 Works by year

2020s

20242 papers20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Survey of Hallucination in Natural Language Generation (2022 · 5,974 citations)
    This survey maps out "hallucination" in neural text generation, meaning output that sounds fluent but is nonsensical or not faithful to its source.
  2. Measuring Political Bias in Large Language Models: What Is Said and How It Is Said (2024 · 76 citations)
    Most bias benchmarks for LLMs focus on gender or race. When political leaning is studied, it is usually probed with human questionnaires such as the Political Compass, which reduce a model to one left–right score. Bang et al. instead examine what models actually write about contested issues. They prompt models to…
  3. LLM Internal States Reveal Hallucination Risk Faced With a Query (2024 · 52 citations)
    LLMs often answer confidently even when they lack the relevant knowledge, which leads to hallucinations. Ji et al. ask whether a model's own hidden representations of a query can signal this risk before any output is generated. They study Llama2 in two settings. The first compares queries the model has likely seen…

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Survey of Hallucination in Natural Language GenerationACM Computing SurveysZiwei Ji, Nayeon Lee, Rita Frieske, et al.2022 · 5,974 citations
  2. Measuring Political Bias in Large Language Models: What Is Said and How It Is SaidAnnual Meeting of the Association for Computational Linguistics (ACL)Yejin Bang, Delong Chen, Nayeon Lee, et al.2024 · 76 citations
  3. LLM Internal States Reveal Hallucination Risk Faced With a QueryarXiv:2407.03282Ziwei Ji, Delong Chen, Etsuko Ishii, et al.2024 · 52 citations
  4. TV2TV: A Unified Framework for Interleaved Language and Video Generation ↗arXiv (Cornell University) · no summary yetXiaochuang Han et al.2025

 Co-authors here

12 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 1,301 times (of the 149,439 papers whose bibliographies we read); 260 citing papers with a page here →.

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