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Yang Zhao

Yang Zhao has 4 papers in our library, in 2025–2026, most of them on Machine translation, Language models & pretraining, Speech & spoken language; often with Bibo Cai, Bing Qin, Li Du.

4 papers in our library · 2025–2026

OpenAlex · h-index 0 · 3 works · 0 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 11 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #9739

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

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.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. TriFine: A Large-Scale Dataset of Vision-Audio-Subtitle for Tri-Modal Machine Translation and Benchmark with Fine-Grained Annotated Tags (2025 · 9 citations)
    Video-guided machine translation (VMT) usually feeds a model dense visual features from many frames. This approach is slow, carries a lot of irrelevant signal, and ignores the soundtrack. The authors argue that compact, targeted cues from both vision and audio work better. To test this, they release TriFine, an…
  2. Decoding the Multimodal Mind: Generalizable Brain-to-Text Translation via Multimodal Alignment and Adaptive Routing (2026 · 1 citation)
    Most brain-to-text decoders map neural recordings onto a single representation type, such as image or text embeddings. This ignores the fact that perception is cross-modal: reading about a barking dog can also evoke sights and sounds. Ye et al. build a unified decoder on Qwen2.5-Omni that addresses this gap.
  3. MAESTRO: Meta-learning Adaptive Estimation of Scalarization Trade-offs for Reward Optimization (2026 · 1 citation)
    GRPO aligns LLMs well when answers can be checked automatically, as in math or code. Open-ended generation has no such check, and its goals often conflict, for example creativity versus factuality. Existing fixes either rely on costly LLM judges or combine cheap heuristic rewards (perplexity, entropy, format) using…
  4. Large Language Models Are Still Misled by Simple Bias Ensembles (2026)
    Recent LLMs handle individual shortcut cues better than before. This paper argues that the real weakness appears when several such cues occur together in one input, which is common in real-world text. Most existing bias benchmarks inject only one bias per sample, and a few inject two. The authors therefore built…

All works

5 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

  1. Consolidation or Adaptation? PRISM: Disentangling SFT and RL Data via Gradient Concentration ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetYang Zhao et al.2026

 Co-authors here

10 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 11 times (of the 149,439 papers whose bibliographies we read); 2 citing papers with a page here →.

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