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Lanqing Hong

Lanqing Hong has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning; often with Jing Li, Boyang Xue, Fei Mi.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 24 · 127 works · 1,739 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 82 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #10569

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

🏢Huawei TechnologiesShenzhen · China2020–2026 · 59 paperscurrent🎓National University of SingaporeSingapore · Singapore2016–2026 · 13 paperscurrent🔬Suzhou Research InstituteSuzhou · China2018–2019 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 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).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputational Theory and Mathematics · 1 workExperimental and Cognitive Psychology · 1 workPsychology · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 1 workReasoning · 1 work

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Self-Error-Instruct: Generalizing from Errors for LLMs Mathematical Reasoning (2025 · 5 citations)
    LLMs still make many mistakes on math problems. Earlier error-driven methods build new training data from single failed examples, so the data tends to stay tied to those instances. Self-Error-Instruct (SEI) instead groups failures by error type. First, a target model is run on the GSM8K and MATH training sets to…
  2. AEQ-Bench: Measuring Empathy of Omni-Modal Large Models (2026)
    Omni-modal large models (OLMs) increasingly talk to users through speech, yet evaluations of empathy usually turn audio into transcripts and ignore how something is said.

All works

5 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. DDP: Diffusion Model for Dense Visual Prediction ↗IEEE/CVF International Conference on Computer Vision (ICCV) · no summary yetYuanfeng Ji et al.2023 · 153 citations
  2. G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model ↗arXiv:2312.11370 · no summary yetJiahui Gao et al.2023 · 76 citations
  3. MetaBEV: Solving Sensor Failures for 3D Detection and Map Segmentation ↗IEEE/CVF International Conference on Computer Vision (ICCV) · no summary yetChongjian Ge et al.2023 · 67 citations
  4. Self-Error-Instruct: Generalizing from Errors for LLMs Mathematical ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Erxin Yu, Jing Li, Ming Liao, et al.2025 · 5 citations
  5. AEQ-Bench: Measuring Empathy of Omni-Modal Large ModelsFindings of the Association for Computational LinguisticsXuan Luo, Lewei Yao, Lanqing Hong, et al.2026

 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 82 times (of the 149,439 papers whose bibliographies we read); 25 citing papers with a page here →.

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