Elamur Research⌕
☰
home / authors / Hexiang Hu

Hexiang Hu

Hexiang Hu has 2 papers in our library, in 2022–2023, most of them on Vision & language, Language models & pretraining, Question answering; often with Alan Ritter, Ming-Wei Chang, Pat Verga.

2 papers in our library · 2022–2023

OpenAlex · ORCID · h-index 26 · 54 works · 4,084 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 2,414 citations in our library · 2 more in our library without a page yet · ⭐ Paper Star rank #4161

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 Southern CaliforniaLos Angeles · United States2017–2022 · 25 papers🏢GoogleMountain View · United States2020–2024 · 9 papers🏢Google DeepMindLondon · United Kingdom2023–2024 · 2 papers🎓Simon Fraser UniversityBurnaby · Canada2016 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 6 Arts and Humanities · 2 works Computer Science · 2 works Artificial Intelligence · 2 works Computer Vision and Pattern Recognition · 2 works Language and Linguistics · 2 works Information Systems · 1 work

🧭 Topics 4 Vision & language · 2 works Language models & pretraining · 1 work Information retrieval & search · 1 work Question answering · 1 work

 Works by year

2020s

20231 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text (2022 · 196 citations)
    Retrieval-augmented language models such as REALM, RAG, FiD and RETRO fetch knowledge from an external index rather than storing everything in their parameters.
  2. Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions? (2023 · 109 citations)
    The paper asks whether current pre-trained vision-language models can handle questions about an image that need specific factual knowledge not visible in the picture, such as when a pictured church was built.

All works

4 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. MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and TextWenhu Chen, Hexiang Hu, Xi Chen, et al.Conference on Empirical Methods in Natural Language Processing (EMNLP) · 196 citations
  2. Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding (original ↗)Kenton Lee et al.2022 · arXiv:2210.03347 · 152 citations · no summary yet
  3. Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?Yang Chen, Hexiang Hu, Yi Luan, et al.Conference on Empirical Methods in Natural Language Processing (EMNLP) · 109 citations
  4. Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? (original ↗)Jinhyuk Lee et al.2024 · arXiv:2406.13121 · 51 citations · no summary yet

 Co-authors here

8 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 2,414 times (of the 141,840 papers whose bibliographies we read); 177 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/hexiang-hu/