Yanbo Wang
Yanbo Wang has 5 papers in our library, in 2024–2026, most of them on Language models & pretraining, Summarization, Speech & spoken language; often with Chao Huang, Deyi Xiong, Dong Yan.
OpenAlex · h-index 0 · 4 works · 0 citations OpenAlex, 2026-10-14
📄 5 works in our library · 🔗 Cited 144 times in our library · ⭐ Paper Stars ranking: #6144
Affiliations
Fields, topics and concepts
📚 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 worksSocial Sciences · 2 worksPolitical Science and International Relations · 1 workSafety Research · 1 workSociology and Political Science · 1 work
🧭 Topics 7Research subjects and languages the work is about.Language models & pretraining · 2 worksBias, fairness & safety · 1 workEvaluation & benchmarks · 1 workSpeech & spoken language · 1 workEnglish · 1 workLegal AI · 1 workSummarization · 1 work
Works by year
2020s
Papers
- Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge (2024 · 148 citations)
LLMs are now routinely used to grade other models' outputs, both in benchmarks and as reward signals during training. - Audio Jailbreak: An Open Comprehensive Benchmark for Jailbreaking Large Audio-Language Models (2026 · 3 citations)
Large Audio-Language Models (LAMs) can be pushed into producing harmful content, yet their safety has rarely been measured systematically. - What If Consensus Lies? Selective-Complementary Reinforcement Learning at Test Time (2026 · 2 citations)
Test-Time Reinforcement Learning (TTRL) lets LLMs improve their reasoning on unlabeled test questions by using the majority-voted answer among sampled rollouts as a pseudo-reward. - APPSI-139: A Parallel Corpus of English Application Privacy Policy Summarization and Interpretation (2026)
App privacy policies are long and full of legal and technical jargon, so users often accept terms they do not understand. - Breaking the Impasse: Dual-Scale Evolutionary Policy Training for Social Language Agents (2026)
The paper looks at what goes wrong when self-play reinforcement learning with verifiable rewards (RLVR) moves from closed tasks like math or code to open-ended social language games.
Co-authors here
- Chao Huang
- Deyi Xiong
- Dong Yan
- Dongping Chen
- Kui Ren
- Lang Gao
- Lu Wang
- Minzheng Wang
- Nan Xu
- Nitesh V Chawla
- Nuno Moniz
- Pin-Yu Chen
Venues
Cited by
Updated
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