Rui Qi
Rui Qi has 2 papers in our library, in 2026, most of them on Multilingual & cross-lingual NLP, Low-resource languages, Reasoning; often with Fengran Mo, Hongliang Li, Jian-Yun Nie.
OpenAlex · ORCID · h-index 0 · 9 works · 0 citations OpenAlex, 2026-10-14
📄 2 works in our library · 🔗 Cited 5 times in our library · ⭐ Paper Stars ranking: #11858
Affiliations
Fields, topics and concepts
📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 work
🧭 Topics 4Research subjects and languages the work is about.Information retrieval & search · 1 workMultilingual & cross-lingual NLP · 1 workLow-resource languages · 1 workReasoning · 1 work
Works by year
2020s
Papers
- 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… - Evidence-Augmented Generation Reasoning for Extremely Low-Resource Language Decipherment (2026)
The paper addresses Rosetta Stone-style puzzles from linguistic Olympiads. A model sees a few sentence pairs in an extremely low-resource or extinct language plus English and must infer grammar and vocabulary to translate new items, match words, or fill in forms. Plain few-shot or chain-of-thought prompting tends to…
Co-authors here
- Fengran Mo
- Hongliang Li
- Jian-Yun Nie
- Jinan Xu
- Kaiyu Huang
- Long Yuan
- Meng Jiang
- Shuo Wang
- Xu Jinan
- Xue Zhang
- Yufeng Chen
Venues
- Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
- Workshop on Multilinguality in the Era of Large Language Models: Workshop on Multilinguality in the Era of Large Language Models 2026
Cited by
Updated
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