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Hung-Ting Chen

Hung-Ting Chen has 3 papers in our library, in 2022–2025, most of them on Question answering, Information retrieval & search; often with Eunsol Choi, Shane Arora, Fangyuan Xu.

3 papers in our library · 2022–2025

OpenAlex · h-index 2 · 6 works · 12 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 122 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6111

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 States2023–2025 · 3 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).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksComputer Vision and Pattern Recognition · 3 worksLanguage and Linguistics · 3 worksInformation Systems · 2 works

🧭 Topics 2Research subjects and languages the work is about.Question answering · 2 worksInformation retrieval & search · 1 work

 Works by year

2020s

20251 paper20231 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence (2022 · 105 citations)
    Open-retrieval QA systems can draw on two kinds of knowledge: facts memorized in a language model's weights and up to a hundred retrieved passages.
  2. CaLMQA: Exploring culturally specific long-form question answering across 23 languages (2025 · 27 citations)
    Most multilingual QA research relies on material translated from English and on short or multiple-choice answers.
  3. Understanding Retrieval Augmentation for Long-Form Question Answering (2023 · 24 citations)
    Retrieval augmentation is widely used to give language models fresh evidence for long-form answers. However, most analyses of how models actually use retrieved text have focused on factoid QA with short, easily scored answers. This paper examines that question for long-form question answering (LFQA) through two…

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. Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting EvidenceConference on Empirical Methods in Natural Language Processing (EMNLP)Hung-Ting Chen, Michael Zhang, Eunsol Choi2022 · 105 citations
  2. CaLMQA: Exploring culturally specific long-form question answering across 23 languagesAnnual Meeting of the Association for Computational Linguistics (ACL)Shane Arora, Marzena Karpinska, Hung-Ting Chen, et al.2025 · 27 citations
  3. Understanding Retrieval Augmentation for Long-Form Question AnsweringarXiv:2310.12150Hung-Ting Chen, Fangyuan Xu, Shane Arora, et al.2023 · 24 citations
  4. RVR: Retrieve-Verify-Retrieve for Comprehensive Question Answering ↗arXiv (Cornell University) · no summary yetDeniz Qian, Hung-Ting Chen, Eunsol Choi2026

 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 122 times (of the 149,439 papers whose bibliographies we read); 29 citing papers with a page here →.

Updated

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