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Yan Zhou

Yan Zhou has 5 papers in our library, in 2025–2026, most of them on Language models & pretraining, Vision & language, Sentiment & emotion; often with Jingping Liu, Qingkai Fang, Yang Feng.

5 papers in our library · 2025–2026

OpenAlex · h-index 0 · 4 works · 0 citations OpenAlex, 2026-10-14

📄 5 works in our library · 🔗 Cited 18 times in our library · ⭐ Paper Stars ranking: #8292

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

🏢China TelecomBeijing · China2026 · 1 papercurrent🎓East China University of Science and TechnologyShanghai · China2026 · 1 papercurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 5 worksComputer Science · 5 worksArtificial Intelligence · 5 worksLanguage and Linguistics · 5 worksComputer Vision and Pattern Recognition · 3 worksSignal Processing · 1 workExperimental and Cognitive Psychology · 1 workPsychology · 1 work

🧭 Topics 7Research subjects and languages the work is about.Language models & pretraining · 3 worksDialogue systems · 1 workSpeech & spoken language · 1 workVision & language · 2 worksMultilingual & cross-lingual NLP · 1 workSentiment & emotion · 1 workReasoning · 1 work

 Works by year

2020s

20263 papers20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. LLaMA-Omni 2: LLM-based Real-time Spoken Chatbot with Autoregressive Streaming Speech Synthesis (2025 · 20 citations)
    The paper addresses how to build a spoken chatbot that is smart, responds quickly, and sounds natural, without the huge speech corpora that native SpeechLMs need.
  2. Can Multimodal Large Language Models Understand Spatial Relations? (2025 · 11 citations)
    Spatial relation reasoning is a basic skill for multimodal large language models (MLLMs), but existing benchmarks have several weaknesses.
  3. Efficient Training for Cross-lingual Speech Language Models (2026 · 2 citations)
    Building end-to-end speech LLMs is difficult because speech data is scarce, particularly outside a few major languages.
  4. TeleAI at SemEval-2026 Task 3: Large Language Models for Dimensional Aspect-Based Sentiment Analysis (2026 · 1 citation)
    This paper describes TeleAI's entry for Track A, Subtask 1 (DimASR) of SemEval-2026 Task 3. In this task, sentiment toward each aspect term is not a polarity label but a pair of continuous Valence–Arousal (VA) scores between 1 and 9, scored with RMSE_VA. The authors treat the problem as direct regression. The input…
  5. MirrorQA: Benchmarking Multimodal LLMs on Mirror-Orientation Reasoning (2026)
    Multimodal LLMs often confuse left and right when a question concerns a person's or animal's own body. They tend to answer from the viewer's side of the image rather than the subject's. Existing spatial benchmarks (e.g., EmbSpatial-Bench, VSR) define directions from the camera's viewpoint, and pose datasets like COCO…

 Co-authors here

12 of 15 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 18 times (of the 149,439 papers whose bibliographies we read); 3 citing papers with a page here →.

Updated

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