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Haozhe Zhao

Haozhe Zhao has 6 papers in our library, in 2023–2026, most of them on Language models & pretraining, Vision & language, Reasoning; often with Baobao Chang, Shuzheng Si, Fanchao Qi.

6 papers in our library · 2023–2026

OpenAlex · ORCID · Wikidata · h-index 5 · 40 works · 225 citations OpenAlex, 2026-10-14

📄 6 works in our library · 🔗 Cited 221 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #4580

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

🎓Peking UniversityBeijing · China2023–2026 · 24 paperscurrent🎓University of Illinois Urbana-ChampaignUrbana · United States2025–2026 · 6 paperscurrent🎓Chengyi College, Jimei UniversityXiamen · China2026 · 1 papercurrent🎓Yancheng Institute of TechnologyYancheng · China2026 · 1 papercurrent🎓Tsinghua UniversityBeijing · China2024–2025 · 6 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 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 5The field of science this work belongs to (OpenAlex fields).Computer Science · 6 worksComputer Vision and Pattern Recognition · 4 worksArts and Humanities · 5 worksArtificial Intelligence · 5 worksLanguage and Linguistics · 5 works

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 3 worksVision & language · 2 worksReasoning · 1 work

 Works by year

2020s

20262 papers20252 papers20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language Models (2024 · 279 citations)
    Large vision-language models (LVLMs) pass hundreds or thousands of visual tokens into their language backbone, which makes inference expensive.
  2. MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning (2023 · 58 citations)
    Most vision-language models (VLMs) built on large language models handle prompts with a single image well.
  3. Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering (2025 · 11 citations)
    Instruction tuning can teach LLMs to hallucinate when the training examples contain knowledge the model never acquired in pre-training.
  4. GATEAU: Selecting Influential Samples for Long Context Alignment (2025 · 5 citations)
    Teaching LLMs to follow instructions over very long inputs is hard because human annotation of long instruction-following data is costly.
  5. A Goal Without a Plan Is Just a Wish: Efficient and Effective Global Planner Training for Long-Horizon Agent Tasks (2026 · 4 citations)
    LLM-based agents on long, multi-step tasks often flail. Without an overall plan, they fall into aimless trial-and-error and produce hallucinated actions. Prior fixes either teach planning implicitly via SFT or RL on the executor, which needs lots of data or converges slowly, or supply explicit guidance that depends…
  6. FaithLens: Detecting and Explaining Faithfulness Hallucination (2026 · 1 citation)
    LLMs often produce claims that contradict, distort, or go beyond the context they were given. This faithfulness hallucination is a problem for RAG and summarization. Current detectors are either expensive prompted LLMs or compact classifiers that return only a binary label, give no reasoning, and generalize unevenly…

All works

7 works of theirs in our library, the most cited first; 6 with a page here, the others link their original until we summarise them.

  1. An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language ModelsLecture notes in computer scienceLiang Chen, Haozhe Zhao, Tianyu Liu, et al.2024 · 279 citations
  2. MMICL: Empowering Vision-language Model with Multi-Modal In-Context LearningarXiv:2309.07915Haozhe Zhao, Zefan Cai, Shuzheng Si, et al.2023 · 58 citations
  3. Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data FilteringAnnual Meeting of the Association for Computational Linguistics (ACL)Shuzheng Si, Haozhe Zhao, Gang Chen, et al.2025 · 11 citations
  4. GATEAU: Selecting Influential Samples for Long Context AlignmentConference on Empirical Methods in Natural Language Processing (EMNLP)Shuzheng Si, Haozhe Zhao, Gang Chen, et al.2025 · 5 citations
  5. A Goal Without a Plan Is Just a Wish: Efficient and Effective Global Planner Training for Long-Horizon Agent TasksAnnual Meeting of the Association for Computational Linguistics (ACL)Shuzheng Si, Haozhe Zhao, Kangyang Luo, et al.2026 · 4 citations
  6. FaithLens: Detecting and Explaining Faithfulness HallucinationFindings of the Association for Computational LinguisticsShuzheng Si, Qingyi Wang, Haozhe Zhao, et al.2026 · 1 citation
  7. MENTOR: Efficient Autoregressive Image Generation with Balanced Multimodal Control ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetHaozhe Zhao et al.2026

 Co-authors here

12 of 20 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 221 times (of the 149,439 papers whose bibliographies we read); 44 citing papers with a page here →.

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