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David Wan

David Wan has 4 papers in our library, in 2022–2026, most of them on Language models & pretraining, Summarization, Text generation; often with Mohit Bansal, Elias Stengel-Eskin, Ido Dagan.

4 papers in our library · 2022–2026

OpenAlex · h-index 0 · 7 works · 0 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 59 times in our library · ⭐ Paper Stars ranking: #6589

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

🎓University of North Carolina at Chapel HillChapel Hill · United States2026 · 6 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 7The field of science this work belongs to (OpenAlex fields).Computer Science · 4 worksArtificial Intelligence · 4 worksArts and Humanities · 3 worksLanguage and Linguistics · 3 worksSocial Sciences · 1 workSociology and Political Science · 1 workComputer Vision and Pattern Recognition · 1 work

🧭 Topics 5Research subjects and languages the work is about.Language models & pretraining · 1 workSummarization · 1 workText generation · 1 workReasoning · 1 workVision & language · 1 work

 Works by year

2020s

20262 papers20251 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization (2022 · 101 citations)
    Abstractive summarizers built on large pre-trained models often hallucinate, producing facts or entities absent from the source document.
  2. LAQuer: Localized Attribution Queries in Content-grounded Generation (2025 · 8 citations)
    Content-grounded generators, such as RAG systems, often produce claims that their sources do not support, so users have to check them.
  3. Localizing Factual Inconsistencies in Attributable Text Generation (2026 · 7 citations)
    LLM outputs that should be grounded in a reference document, such as summaries, open-book answers or cited responses, still contain hallucinations.
  4. DART: Leveraging Multi-Agent Disagreement for Tool Recruitment in Multimodal Reasoning (2026 · 1 citation)
    Debates between vision-language models (VLMs) often stall because the agents share similar perceptual blind spots and bring no new evidence.

 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 59 times (of the 149,439 papers whose bibliographies we read); 7 citing papers with a page here →.

Updated

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