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Zhiqi Huang

Zhiqi Huang has 3 papers in our library, in 2023–2026, most of them on Language models & pretraining, Multilingual & cross-lingual NLP, Low-resource languages; often with Enzhe Lu, James Allan, Puxuan Yu.

3 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 5 · 16 works · 108 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 87 times in our library · ⭐ Paper Stars ranking: #6473

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 Massachusetts AmherstAmherst Center · United States2019–2024 · 10 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 more institutions 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).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 2 worksInformation Systems · 1 work

🧭 Topics 7Research subjects and languages the work is about.Language models & pretraining · 2 worksInformation retrieval & search · 1 workLow-resource languages · 1 workMultilingual & cross-lingual NLP · 1 workEvaluation & benchmarks · 1 workReasoning · 1 workVision & language · 1 work

 Works by year

2020s

20261 paper20251 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. MoBA: Mixture of Block Attention for Long-Context LLMs (2025 · 80 citations)
    Standard softmax attention grows quadratically with sequence length, which makes long-context LLMs expensive to train and run.
  2. Improving Cross-lingual Information Retrieval on Low-Resource Languages via Optimal Transport Distillation (2023 · 46 citations)
    Cross-language retrieval for low-resource languages faces two problems. Multilingual encoders such as mBERT and XLM-R are pre-trained on unevenly distributed data, so they represent these languages poorly. Relevance-labelled CLIR training data for such languages also barely exists. The authors propose OPTICAL, which…
  3. MMErroR: A Benchmark for Erroneous Reasoning in Vision-Language Models (2026 · 1 citation)
    Most vision-language benchmarks score only whether the final answer is correct. They cannot show whether a model actually understands why a line of reasoning breaks down. MMErroR addresses this by testing whether VLMs can spot a flawed multimodal reasoning chain and name the kind of mistake it contains.

 Co-authors here

4 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 87 times (of the 149,439 papers whose bibliographies we read); 13 citing papers with a page here →.

Updated

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