--- title: "Xiaonan Li — author profile and papers" description: "Xiaonan Li: 2 papers on Elamur Research (2020–2023), h-index 26 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/xiaonan-li/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/xiaonan-li/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Xiaonan Li # Xiaonan Li Xiaonan Li works in natural language processing. Their papers here, from 2020 to 2023, deal with [named entity recognition](https://md.research.elamur.ai/free/en/topics/named-entity-recognition/) for Chinese text and with how [language models](https://md.research.elamur.ai/free/en/topics/language-models/) can be helped by better choices of input. Both lines of work draw on Transformer-based architectures and on ideas from [information retrieval](https://md.research.elamur.ai/free/en/topics/information-retrieval/). A recurring concern is replacing complicated or task-specific machinery with simpler, more general designs that run efficiently. The most cited of these papers is [FLAT: Chinese NER Using Flat-Lattice Transformer](https://md.research.elamur.ai/free/en/papers/li-2020-flat-chinese-ner-flat-lattice/). Chinese has no spaces between words, so entity recognition there often relies on lattices that combine characters with words matched from a lexicon. Earlier lattice models were recurrent or structurally intricate and made poor use of GPU parallelism. FLAT turns the lattice into a flat collection of spans, each marked by where it begins and ends. A relative position encoding built from these boundaries lets a Transformer capture how the spans relate to one another. The later work moves from sequence labelling to in-context learning, where a language model solves a task from a few examples placed in its prompt. [Unified Demonstration Retriever for In-Context Learning](https://md.research.elamur.ai/free/en/papers/li-2023-unified-demonstration-retriever-context-learning/) (2023) addresses the fact that results depend strongly on which examples are picked. Instead of generic similarity search or a separately trained retriever for every task, it trains a single bi-encoder shared across many tasks. Each example is prefixed with a short task instruction, and training signals from different tasks are brought into one common form. The bylines of Li's papers include [Fudan University](https://md.research.elamur.ai/free/en/universities/fudan-university/) among their affiliations. Co-authors on the site include [Xipeng Qiu](https://md.research.elamur.ai/free/en/authors/xipeng-qiu/), [Hang Yan](https://md.research.elamur.ai/free/en/authors/hang-yan/) and [Xuanjing Huang](https://md.research.elamur.ai/free/en/authors/xuanjing-huang/). Xiaonan Li has 2 papers in our library, in 2020–2023, most of them on [Named entity recognition](https://md.research.elamur.ai/free/en/topics/named-entity-recognition/), [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Information retrieval & search](https://md.research.elamur.ai/free/en/topics/information-retrieval/); often with [Hang Yan](https://md.research.elamur.ai/free/en/authors/hang-yan/), [Xipeng Qiu](https://md.research.elamur.ai/free/en/authors/xipeng-qiu/), [Xuanjing Huang](https://md.research.elamur.ai/free/en/authors/xuanjing-huang/). 2 papers in our library · 2020–2023 [OpenAlex](https://openalex.org/A5100711201) · [ORCID](https://orcid.org/0000-0003-3661-2394) · [**h-index 26**](https://openalex.org/authors/A5100711201) · [151 works](https://openalex.org/works?filter=author.id:A5100711201) · [2,653 citations](https://openalex.org/works?filter=author.id:A5100711201&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [2 works in our library](https://md.research.elamur.ai/free/en/authors/xiaonan-li/#papers) · [217 citations in our library](https://md.research.elamur.ai/free/en/authors/xiaonan-li/cited-by/) · 2 more works in our library · [⭐ Paper Star rank #2598](https://md.research.elamur.ai/free/en/paper-stars/page/26/#star-2598) 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=Xiaonan+Li) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Xiaonan+Li) · [dblp](https://dblp.org/search?q=Xiaonan+Li) · [ACL Anthology](https://aclanthology.org/search/?q=Xiaonan+Li) ## Affiliations [🔬**National Laboratory of the Rockies** Golden · United States 1988–2010 · 27 papers](https://ror.org/036266993) · [🎓**Fudan University** Shanghai · China 2020–2024 · 17 papers](https://md.research.elamur.ai/free/en/universities/fudan-university/) · [🎓**Lanzhou University of Technology** Lanzhou · China 2021–2025 · 15 papers](https://ror.org/03panb555) · [🏛️**Chinese Academy of Sciences** Beijing · China 2019–2025 · 9 papers](https://md.research.elamur.ai/free/en/government/chinese-academy-of-sciences/) · [🎓**Southwest Jiaotong University** Chengdu · China 2018–2022 · 8 papers](https://ror.org/00hn7w693) · [🎓**China University of Mining and Technology - Beijing** Beijing 2021–2025 · 7 papers](https://ror.org/003ncxf91) · [🔬**Institute of Electrical Engineering** Beijing · China 2018–2025 · 6 papers](https://ror.org/033js6g46) · [🔬**Institute of Metal Research** Shenyang · China 2022–2024 · 4 papers](https://ror.org/03pa1rf77) · [🎓**University of Science and Technology of China** Hefei · China 2022–2024 · 4 papers](https://md.research.elamur.ai/free/en/universities/university-of-science-and-technology-of-china/) · [🎓**Shijiazhuang Institute of Railway Technology** Shijiazhuang · China 2010–2025 · 3 papers](https://ror.org/009vckt70) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 37 more institutions on single bylines are not listed. ## Topics [Named entity recognition 1](https://md.research.elamur.ai/free/en/topics/named-entity-recognition/) · [Language models & pretraining 1](https://md.research.elamur.ai/free/en/topics/language-models/) · [Information retrieval & search 1](https://md.research.elamur.ai/free/en/topics/information-retrieval/) ## Works by year ### 2020s [**2023** 1 paper](https://md.research.elamur.ai/free/en/papers/li-2023-unified-demonstration-retriever-context-learning/) · [**2020** 1 paper](https://md.research.elamur.ai/free/en/papers/li-2020-flat-chinese-ner-flat-lattice/) ## Papers The most cited first (OpenAlex and our library). 1. **[FLAT: Chinese NER Using Flat-Lattice Transformer](https://md.research.elamur.ai/free/en/papers/li-2020-flat-chinese-ner-flat-lattice/)** *(2020 · 503 citations)* Chinese NER is difficult because the text has no explicit word boundaries. Lexicon-matched character-word lattices help, but earlier lattice models such as lattice LSTM, LR-CNN and the GNN-based LGN and CGN are either recurrent or structurally complex. This makes them slow and poorly suited to GPU parallelism. The… 2. **[Unified Demonstration Retriever for In-Context Learning](https://md.research.elamur.ai/free/en/papers/li-2023-unified-demonstration-retriever-context-learning/)** *(2023 · 144 citations)* In-context learning (ICL) lets a language model solve a task from a few input-output examples placed in its prompt, but its accuracy depends heavily on which examples are chosen. ## Also in our library 2 more works of theirs are in our library without a page here yet; they will appear as we publish them. ## Co-authors here 3 co-authors with a page, the most papers together first. - [Hang Yan](https://md.research.elamur.ai/free/en/authors/hang-yan/) ×2 - [Xipeng Qiu](https://md.research.elamur.ai/free/en/authors/xipeng-qiu/) ×2 - [Xuanjing Huang](https://md.research.elamur.ai/free/en/authors/xuanjing-huang/) ## Cited by Papers of our library cite their works 217 times (of the 140,884 papers whose bibliographies we read); [6 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/xiaonan-li/cited-by/).