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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.

4 papers in our library · 2026

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

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. 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…
  2. 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…
  3. 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…
  4. 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

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. 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

12 of 16 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 17 times (of the 149,439 papers whose bibliographies we read); 6 citing papers with a page here →.

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