--- title: "Yulu Xie · Guangxi University — author profile and papers" description: "Yulu Xie, Guangxi University: 2 papers on Elamur Research (2026), h-index 1 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/yulu-xie/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/yulu-xie/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Yulu Xie # Yulu Xie Yulu Xie has 2 papers in our library, in 2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Information extraction](https://md.research.elamur.ai/free/en/topics/information-extraction/), [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/); often with [Chong Zhang](https://md.research.elamur.ai/free/en/authors/chong-zhang/), [Huan Zhao](https://md.research.elamur.ai/free/en/authors/huan-zhao/), [Mingxu Chai](https://md.research.elamur.ai/free/en/authors/mingxu-chai/). 2 papers in our library · 2026 [OpenAlex](https://openalex.org/A5013315914) · [ORCID](https://orcid.org/0000-0003-0621-7748) · [**h-index 1**](https://openalex.org/authors/A5013315914) · [4 works](https://openalex.org/works?filter=author.id:A5013315914) · [13 citations](https://openalex.org/works?filter=author.id:A5013315914&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/yulu-xie/#papers) · 🔗 Cited 2 times in our library · [⭐ Paper Stars ranking: #12076](https://md.research.elamur.ai/free/en/paper-stars/page/121/#star-12076) 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=Yulu+Xie) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Yulu+Xie) · [dblp](https://dblp.org/search?q=Yulu+Xie) · [ACL Anthology](https://aclanthology.org/search/?q=Yulu+Xie) ## Affiliations [🎓**Guangxi University** Nanning · China 2026 · 1 paper*current*](https://md.research.elamur.ai/free/en/universities/guangxi-university/) · [🎓**Tsinghua University** Beijing · China 2026 · 1 paper*current*](https://md.research.elamur.ai/free/en/universities/tsinghua-university/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions 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 · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Computer Vision and Pattern Recognition · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Language and Linguistics · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) 🧭 Topics 4 Research subjects and languages the work is about.[Information extraction · 1 work](https://md.research.elamur.ai/free/en/topics/information-extraction/) · [Evaluation & benchmarks · 1 work](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · [Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/) · [Reasoning · 1 work](https://md.research.elamur.ai/free/en/topics/reasoning/) ## Works by year ### 2020s [**2026** 2 papers](https://md.research.elamur.ai/free/en/authors/yulu-xie/year/2026/) ## Papers The most cited first (OpenAlex and our library). 1. **[EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving](https://md.research.elamur.ai/free/en/papers/zhou-2026-engibench-benchmark-evaluating-large-language/)** *(2026 · 3 citations)* Math benchmarks such as GSM8K and MATH test LLMs on clean, fully specified problems. Real engineering work is different: it involves physical context, missing information, competing goals and uncertainty. EngiBench is designed to measure how LLMs handle such tasks and to diagnose where they fail. Problems from many… 2. **[Unveiling the Deficiencies of Pre-trained Text-and-Layout Models in Real-world Visually-rich Document Information Extraction](https://md.research.elamur.ai/free/en/papers/zhang-2026-unveiling-deficiencies-pre-trained-text/)** *(2026 · 1 citation)* Pre-trained text-and-layout models (PTLMs) score very highly on standard benchmarks for extracting information from visually-rich documents (VrD-IE), but they disappoint in practice. ## Co-authors here 9 co-authors with a page, the most papers together first. - [Chong Zhang](https://md.research.elamur.ai/free/en/authors/chong-zhang/) - [Huan Zhao](https://md.research.elamur.ai/free/en/authors/huan-zhao/) - [Mingxu Chai](https://md.research.elamur.ai/free/en/authors/mingxu-chai/) - [Qi Zhang](https://md.research.elamur.ai/free/en/authors/qi-zhang/) - [Wenxuan Liu](https://md.research.elamur.ai/free/en/authors/wenxuan-liu/) - [Yan Xu](https://md.research.elamur.ai/free/en/authors/yan-xu/) - [Yang Wu](https://md.research.elamur.ai/free/en/authors/yang-wu/) - [Yue Zhang](https://md.research.elamur.ai/free/en/authors/yue-zhang/) - [Yuheng Cheng](https://md.research.elamur.ai/free/en/authors/yuheng-cheng/) ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [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 2 times (of the 149,439 papers whose bibliographies we read). Updated 2 November 2026