Yubo Wang
Yubo Wang has 3 papers in our library, in 2024–2026, most of them on Reasoning, Language models & pretraining, Evaluation & benchmarks; often with Bo Li, Ge Zhang, Graham Neubig.
OpenAlex · ORCID · h-index 9 · 26 works · 412 citations OpenAlex, 2026-10-14
📄 3 works in our library · 🔗 Cited 675 times in our library · ⭐ Paper Stars ranking: #2854
Affiliations
Fields, topics and concepts
📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksComputer Vision and Pattern Recognition · 3 worksArts and Humanities · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksMathematics · 1 work
🧭 Topics 4Research subjects and languages the work is about.Reasoning · 3 worksEvaluation & benchmarks · 1 workLanguage models & pretraining · 1 workVision & language · 1 work
💡 Concepts 1Key ideas and methods explained on our concept pages.Natural language understanding · 1 work
Works by year
2020s
Papers
- MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark (2024 · 761 citations)
MMLU had become saturated as a benchmark. Frontier LLMs all clustered around 86–87%, scores shifted noticeably when the prompt changed, and part of the data was noisy or wrongly labelled. The authors built MMLU-Pro to give the field a harder test that separates models better. - MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale (2025 · 40 citations)
Open-source multimodal LLMs tend to struggle with complex reasoning. A major reason is that their instruction-tuning data is mostly recycled from academic VQA-style sets such as VQA, AI2D, and ChartQA, which offer short answers and no step-by-step justification. The authors propose a low-cost pipeline built entirely… - Forest Before Trees: Latent Superposition for Efficient Visual Reasoning (2026 · 3 citations)
Chain-of-Thought helps vision-language models reason step by step. However, spelling out rationales as discrete text loses much of the continuous visual signal. Existing latent-reasoning methods avoid text but still train each hidden state to predict one exact next token, which pushes the model to commit to specific…
Co-authors here
- Bo Li
- Ge Zhang
- Graham Neubig
- Juntian Zhang
- Max Ku
- Nils Lukas
- Tianle Li
- Tianyu Zheng
- Weiming Ren
- Wenhu Chen
- Xiang Yue
- Xueguang Ma
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL) ×2: ACL 2026, ACL 2025
- Conference on Neural Information Processing Systems (NeurIPS): NeurIPS 2024
Cited by
Updated
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