Kaiyu Huang
Kaiyu Huang has 4 papers in our library, in 2026, most of them on Multilingual & cross-lingual NLP, Language models & pretraining, Evaluation & benchmarks; often with Xu Jinan, Fengran Mo, Xue Zhang.
OpenAlex · h-index 1 · 8 works · 13 citations OpenAlex, 2026-10-14
📄 4 works in our library · 🔗 Cited 17 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #9256
Affiliations
Fields, topics and concepts
📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 2 worksInformation Systems · 1 workSocial Sciences · 1 workSafety Research · 1 work
🧭 Topics 7Research subjects and languages the work is about.Multilingual & cross-lingual NLP · 2 worksReasoning · 1 workInformation retrieval & search · 1 workBias, fairness & safety · 1 workEvaluation & benchmarks · 1 workLanguage models & pretraining · 1 workVision & language · 1 work
Works by year
2020s
Papers
- Think Natively: Unlocking Multilingual Reasoning with Consistency-Enhanced Reinforcement Learning (2026 · 11 citations)
Reasoning-oriented LLMs that think before answering often handle non-English prompts poorly. They tend to drift into English or a mix of languages during reasoning, and their accuracy drops compared with English. The authors introduce M-Thinker, a family of models (1.5B, 4B, 7B) trained with GRPO using two new reward… - Language-Coupled Reinforcement Learning for Multilingual Retrieval-Augmented Generation (2026 · 5 citations)
The paper addresses two problems in multilingual retrieval-augmented generation (MRAG). The first is knowledge bias: an LLM answers the same question differently depending on its language. The second is knowledge conflict: passages retrieved from collections in many languages contradict each other. The authors… - When Helpers Become Hazards: A Benchmark for Analyzing Multimodal LLM-Powered Safety in Daily Life (2026 · 1 citation)
Multimodal LLMs are increasingly used as everyday assistants. Their answers can steer people into risky actions even when the user has no harmful intent. Most existing safety benchmarks target jailbreaks or explicit harm, and implicit-risk sets tend to be small or artificial. The authors introduce SaLAD, a benchmark… - SiLP: Enhancing Non-Dominant Language Capabilities with a Selective Bidirectional Language Projection Framework (2026)
Large language models handle English and other dominant languages much better than under-represented ones because their pretraining data is skewed.
All works
- Think Natively: Unlocking Multilingual Reasoning with Consistency-Enhanced Reinforcement LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Xue Zhang, Yunlong Liang, Fandong Meng, et al.2026 · 11 citations
- Language-Coupled Reinforcement Learning for Multilingual Retrieval-Augmented GenerationFindings of the Association for Computational LinguisticsRui Qi, Fengran Mo, Yufeng Chen, et al.2026 · 5 citations
- When Helpers Become Hazards: A Benchmark for Analyzing Multimodal LLM-Powered Safety in Daily LifeFindings of the Association for Computational LinguisticsXinyue Lou, Xu Jinan, Jingyi Yin, et al.2026 · 1 citation
- SiLP: Enhancing Non-Dominant Language Capabilities with a Selective Bidirectional Language Projection FrameworkAnnual Meeting of the Association for Computational Linguistics (ACL)Junpeng Liu, Jiuyi Li, Kaiyu Huang, et al.2026
- One Pair Suffices: Unlocking Universal Zero-Shot Translation via Cross-Architecture Alignment ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetHao Zong et al.2026
Co-authors here
- Xu Jinan ×3
- Fengran Mo ×2
- Xue Zhang ×2
- Yufeng Chen ×2
- Degen Huang
- Fandong Meng
- Hui Xiong
- Jian-Yun Nie
- Jie Zhou
- Meng Jiang
- Shuo Wang
- Songming Zhang
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL) ×3: ACL 2026 ×2
- Findings of the Association for Computational Linguistics ×2: Findings of the Association for Computational Linguistics 2026 ×2
Cited by
Updated
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