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I-Chun Chern

I-Chun Chern has 2 papers in our library, in 2023; often with Junxian He, Pengfei Liu, Shiqi Chen.

2 papers in our library · 2023

OpenAlex · h-index 5 · 6 works · 72 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 222 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #5900

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

🔬Academia SinicaTaipei · Taiwan2025 · 1 papercurrent🔬Research Center for Information Technology Innovation, Academia SinicaTaipei · Taiwan2025 · 1 papercurrent🎓Carnegie Mellon UniversityPittsburgh · United States2023 · 4 papers

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 · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksSocial Sciences · 2 worksSociology and Political Science · 2 works

 Works by year

2020s

20232 papers

 Papers

The most cited first (OpenAlex and our library).

  1. FacTool: Factuality Detection in Generative AI — A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios (2023 · 160 citations)
    Large language models such as ChatGPT write fluent long-form answers that may contain factual errors. Classic fact-checking setups rarely fit this case. They usually assume that the claim to check, or the evidence for it, is already given. LLM outputs instead come without either, and they are long, with no clear unit…
  2. FELM: Benchmarking Factuality Evaluation of Large Language Models (2023 · 99 citations)
    Detecting factual errors in LLM output is important, but the tools that do this detection also need to be measured.

All works

3 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. FacTool: Factuality Detection in Generative AI — A Tool Augmented Framework for Multi-Task and Multi-Domain ScenariosarXiv:2307.13528I-Chun Chern, Steffi Chern, Shiqi Chen, et al.2023 · 160 citations
  2. FELM: Benchmarking Factuality Evaluation of Large Language ModelsConference on Neural Information Processing Systems (NeurIPS)Shiqi Chen, Yiran Zhao, Jinghan Zhang, et al.2023 · 99 citations
  3. Improving Factuality of Abstractive Summarization via Contrastive Reward Learning ↗Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023) · no summary yetI-chun Chern et al.2023 · 19 citations

 Co-authors here

10 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 222 times (of the 149,439 papers whose bibliographies we read); 63 citing papers with a page here →.

Updated

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