--- title: "Dandan Tu · Huawei Technologies — author profile and papers" description: "Dandan Tu, Huawei Technologies: 4 papers on Elamur Research (2026), h-index 7 — affiliations, topics, co-authors and who cites them in…" canonical: "https://research.elamur.ai/free/en/authors/dandan-tu/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/dandan-tu/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Dandan Tu # Dandan Tu Dandan Tu has 4 papers in our library, in 2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/), [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/); often with [Bing Qin](https://md.research.elamur.ai/free/en/authors/bing-qin/), [Xiaocheng Feng](https://md.research.elamur.ai/free/en/authors/xiaocheng-feng/), [Yunfei Lu](https://md.research.elamur.ai/free/en/authors/yunfei-lu/). 4 papers in our library · 2026 [OpenAlex](https://openalex.org/A5101645793) · [ORCID](https://orcid.org/0000-0002-3560-124X) · [Wikidata](https://www.wikidata.org/wiki/Q131016957) · [**h-index 7**](https://openalex.org/authors/A5101645793) · [25 works](https://openalex.org/works?filter=author.id:A5101645793) · [154 citations](https://openalex.org/works?filter=author.id:A5101645793&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 4 works in our library](https://md.research.elamur.ai/free/en/authors/dandan-tu/#papers) · 🔗 Cited 6 times in our library · [1 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #10227](https://md.research.elamur.ai/free/en/paper-stars/page/103/#star-10227) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Dandan+Tu) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Dandan+Tu) · [dblp](https://dblp.org/search?q=Dandan+Tu) · [ACL Anthology](https://aclanthology.org/search/?q=Dandan+Tu) ## Affiliations [🏢**Huawei Technologies** Shenzhen · China 2018–2026 · 10 papers*current*](https://md.research.elamur.ai/free/en/companies/huawei-technologies/) · [🏛️**Chinese Academy of Sciences** Beijing · China 2009–2014 · 3 papers](https://md.research.elamur.ai/free/en/government/chinese-academy-of-sciences/) · [🔬**Institute of Computing Technology** Beijing · China 2010–2014 · 2 papers](https://md.research.elamur.ai/free/en/labs/institute-of-computing-technology/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed. ## Fields, topics and concepts Of its works in our library, with the number of works on each. 📚 Shelves 5 The field of science this work belongs to (OpenAlex fields).[Arts and Humanities · 4 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 4 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 4 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Language and Linguistics · 4 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Vision and Pattern Recognition · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) 🧭 Topics 5 Research subjects and languages the work is about.[Reasoning · 2 works](https://md.research.elamur.ai/free/en/topics/reasoning/) · [Evaluation & benchmarks · 2 works](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · [Machine translation · 1 work](https://md.research.elamur.ai/free/en/topics/machine-translation/) · [Language models & pretraining · 2 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Multilingual & cross-lingual NLP · 1 work](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) ## Works by year ### 2020s [**2026** 4 papers](https://md.research.elamur.ai/free/en/authors/dandan-tu/year/2026/) ## Papers The most cited first (OpenAlex and our library). 1. **[Unlocking Multilingual Reasoning Capability of LLMs and LVLMs through Representation Engineering](https://md.research.elamur.ai/free/en/papers/li-2026-unlocking-multilingual-reasoning-capability-llms/)** *(2026 · 4 citations)* Large language models and vision-language models reason noticeably worse in non-English languages, especially low-resource ones such as Thai and Swahili. 2. **[Culture-Aware Machine Translation in Large Language Models: Benchmarking and Investigation](https://md.research.elamur.ai/free/en/papers/yuan-2026-culture-aware-machine-translation-large/)** *(2026 · 2 citations)* LLMs translate general text well, but little is known about how they handle culturally loaded content. The authors build CanMT, a parallel benchmark drawn from classic novels and their professional translations. It covers 12 directions among English, Chinese, Japanese, Spanish and Russian. 3. **[x1: Learning to Think Adaptively Across Languages and Cultures](https://md.research.elamur.ai/free/en/papers/ye-2026-x1-learning-think-adaptively-across/)** *(2026 · 1 citation)* Most reasoning LLMs think mainly in one dominant language, usually English, even when a question is posed in another language or depends on a specific culture. 4. **[MDC-Bench: A Multidisciplinary Causal Benchmark Based on Causal Structures for Evaluating Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-mdc-bench-multidisciplinary-causal-benchmark/)** *(2026)* Most existing causal benchmarks for LLMs use everyday commonsense scenarios with short, single-hop cause–effect links. ## 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. **[Unlocking Multilingual Reasoning Capability of LLMs and LVLMs through Representation Engineering](https://md.research.elamur.ai/free/en/papers/li-2026-unlocking-multilingual-reasoning-capability-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qiming Li, Xiaocheng Feng, Yixuan Ma, et al.*2026 · 4 citations* 2. **[Culture-Aware Machine Translation in Large Language Models: Benchmarking and Investigation](https://md.research.elamur.ai/free/en/papers/yuan-2026-culture-aware-machine-translation-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zekun Yuan, Yangfan Ye, Xiaocheng Feng, et al.*2026 · 2 citations* 3. **[x1: Learning to Think Adaptively Across Languages and Cultures](https://md.research.elamur.ai/free/en/papers/ye-2026-x1-learning-think-adaptively-across/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yangfan Ye, Xiaocheng Feng, Xiachong Feng, et al.*2026 · 1 citation* 4. **[MDC-Bench: A Multidisciplinary Causal Benchmark Based on Causal Structures for Evaluating Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-mdc-bench-multidisciplinary-causal-benchmark/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Peng Wang, Yuxiong Yan, Xiao Ding, et al.*2026* 5. **[TinyJudge: Unverifiable Constraint Alignment via Lightweight Specialist Ensembles ↗](https://aclanthology.org/2026.acl-long.1204/) Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yet**Yirong Zeng et al.*2026* ## Co-authors here 12 of 17 co-authors with a page, the most papers together first. - [Bing Qin](https://md.research.elamur.ai/free/en/authors/bing-qin/) ×3 - [Xiaocheng Feng](https://md.research.elamur.ai/free/en/authors/xiaocheng-feng/) ×3 - [Yunfei Lu](https://md.research.elamur.ai/free/en/authors/yunfei-lu/) ×3 - [Qichen Hong](https://md.research.elamur.ai/free/en/authors/qichen-hong/) ×2 - [Xiachong Feng](https://md.research.elamur.ai/free/en/authors/xiachong-feng/) ×2 - [Yangfan Ye](https://md.research.elamur.ai/free/en/authors/yangfan-ye/) ×2 - [Zekun Yuan](https://md.research.elamur.ai/free/en/authors/zekun-yuan/) ×2 - [Bibo Cai](https://md.research.elamur.ai/free/en/authors/bibo-cai/) - [Chao Peng](https://md.research.elamur.ai/free/en/authors/chao-peng/) - [Kai Xiong](https://md.research.elamur.ai/free/en/authors/kai-xiong/) - [Lei Huang](https://md.research.elamur.ai/free/en/authors/lei-huang/) - [Libo Qin](https://md.research.elamur.ai/free/en/authors/libo-qin/) ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/) ×3: [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/) ×2 - [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/) ×2: [Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/) ×2 ## Cited by Papers of our library cite their works 6 times (of the 149,439 papers whose bibliographies we read). Updated 1 November 2026