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Chongyi Wang

Chongyi Wang has 4 papers in our library, in 2023–2026, most of them on Language models & pretraining, Multilingual & cross-lingual NLP, Vision & language; often with Tianyu Yu, Junbo Cui, Tianchi Cai.

4 papers in our library · 2023–2026

OpenAlex · h-index 5 · 10 works · 117 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 547 times in our library · ⭐ Paper Stars ranking: #4695

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

 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 · 4 worksArtificial Intelligence · 4 worksComputer Vision and Pattern Recognition · 4 worksArts and Humanities · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 1 workMultilingual & cross-lingual NLP · 1 workVision & language · 1 work

 Works by year

2020s

20261 paper20251 paper20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. MiniCPM-V: A GPT-4V Level MLLM on Your Phone (2024 · 269 citations)
    Most multimodal LLMs are so large and compute-hungry that they must run on cloud servers. This rules out many mobile, offline, energy-limited and privacy-sensitive uses. The MiniCPM-V Team (OpenBMB) introduces MiniCPM-V, an open-source family of compact MLLMs built for end-side hardware such as smartphones and aimed…
  2. Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages (2023 · 43 citations)
    Most large multimodal systems for image understanding and image generation work well only in English. Other languages lack the huge image-text collections such training needs: BLIP-2 used over 100M pairs, and Stable Diffusion used over 2B. The authors propose MPM, a training paradigm that uses a multilingual LLM as a…
  3. MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe (2025 · 24 citations)
    MiniCPM-V 4.5 is an 8B-parameter multimodal LLM from OpenBMB. It targets three costly parts of MLLM development: long visual token sequences, brittle document-parsing pipelines and verbose reasoning. To shorten visual input, the authors extend the 2D-Resampler of earlier MiniCPM-V models into a unified 3D-Resampler.…
  4. MiniCPM-o 4.5: Towards Real-Time Full-Duplex Omni-Modal Interaction (2026 · 5 citations)
    Current multimodal LLMs mostly handle interaction in turns: they first take in input, then answer, and they only act when the user explicitly asks.

 Co-authors here

12 of 20 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 547 times (of the 149,439 papers whose bibliographies we read); 124 citing papers with a page here →.

Updated

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