--- title: "Xi Victoria Lin — author profile and papers" description: "Xi Victoria Lin, University of Waterloo: 10 papers on Elamur Research (2019–2023), h-index 12 — affiliations, topics, co-authors and who cites them in…" canonical: "https://research.elamur.ai/free/en/authors/xi-victoria-lin/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/xi-victoria-lin/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Xi Victoria Lin # Xi Victoria Lin Xi Victoria Lin has 10 papers in our library, in 2019–2023, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Code & programming languages](https://md.research.elamur.ai/free/en/topics/code-and-programming/), [Semantic parsing & meaning representation](https://md.research.elamur.ai/free/en/topics/semantic-parsing/); often with [Mikel Artetxe](https://md.research.elamur.ai/free/en/authors/mikel-artetxe/), [Naman Goyal](https://md.research.elamur.ai/free/en/authors/naman-goyal/), [Rui Zhang](https://md.research.elamur.ai/free/en/authors/rui-zhang/). 10 papers in our library · 2019–2023 current affiliation [University of Waterloo](https://md.research.elamur.ai/free/en/universities/university-of-waterloo/) · Waterloo Canada [OpenAlex](https://openalex.org/A5062266757) · [**h-index 12**](https://openalex.org/authors/A5062266757) · [21 works](https://openalex.org/works?filter=author.id:A5062266757) · [1,397 citations](https://openalex.org/works?filter=author.id:A5062266757&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [10 works in our library](https://md.research.elamur.ai/free/en/authors/xi-victoria-lin/#papers) · [3,430 citations in our library](https://md.research.elamur.ai/free/en/authors/xi-victoria-lin/cited-by/) · 2 more works in our library · [⭐ Paper Star rank #669](https://md.research.elamur.ai/free/en/paper-stars/page/7/#star-669) 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=Xi+Victoria+Lin) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Xi+Victoria+Lin) · [dblp](https://dblp.org/search?q=Xi+Victoria+Lin) · [ACL Anthology](https://aclanthology.org/search/?q=Xi+Victoria+Lin) ## Affiliations [🎓**University of Waterloo** Waterloo · Canada 2025 · 1 paper*current*](https://md.research.elamur.ai/free/en/universities/university-of-waterloo/) · [🏢**Meta** Menlo Park · United States 2021–2023 · 7 papers](https://md.research.elamur.ai/free/en/companies/meta/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. ## Topics [Language models & pretraining 5](https://md.research.elamur.ai/free/en/topics/language-models/) · [Code & programming languages 3](https://md.research.elamur.ai/free/en/topics/code-and-programming/) · [Semantic parsing & meaning representation 3](https://md.research.elamur.ai/free/en/topics/semantic-parsing/) · [Multilingual & cross-lingual NLP 2](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) · [Question answering 1](https://md.research.elamur.ai/free/en/topics/question-answering/) · [Dialogue systems 1](https://md.research.elamur.ai/free/en/topics/dialogue-systems/) · [Information retrieval & search 1](https://md.research.elamur.ai/free/en/topics/information-retrieval/) ## Works by year ### 2020s [**2023** 2 papers](https://md.research.elamur.ai/free/en/authors/xi-victoria-lin/year/2023/) · [**2022** 4 papers](https://md.research.elamur.ai/free/en/authors/xi-victoria-lin/year/2022/) · [**2021** 1 paper](https://md.research.elamur.ai/free/en/papers/lin-2021-few-shot-learning-multilingual-language/) · [**2020** 1 paper](https://md.research.elamur.ai/free/en/papers/lin-2020-bridging-textual-tabular-data-cross/) ### 2010s [**2019** 2 papers](https://md.research.elamur.ai/free/en/authors/xi-victoria-lin/year/2019/) ## Papers The most cited first (OpenAlex and our library). 1. **[OPT: Open Pre-trained Transformer Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2022-opt-open-pre-trained-transformer/)** *(2022 · 1,709 citations)* The largest language models are very expensive to train, so only a few labs can reproduce them. Models available through paid APIs do not provide access to their weights, which makes them hard to study. The authors from Meta AI created OPT, a suite of autoregressive decoder-only transformers ranging from 125M to 175B… 2. **[Few-shot Learning with Multilingual Language Models](https://md.research.elamur.ai/free/en/papers/lin-2021-few-shot-learning-multilingual-language/)** *(2021 · 496 citations)* Generative language models such as GPT-3 can pick up new tasks from a few in-context examples, but their training text is overwhelmingly English, which limits their usefulness for other languages. 3. **[Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing](https://md.research.elamur.ai/free/en/papers/lin-2020-bridging-textual-tabular-data-cross/)** *(2020 · 261 citations)* Cross-database text-to-SQL parsing means converting a natural-language question into an executable SQL query for a relational database the model never saw during training. 4. **[CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases](https://md.research.elamur.ai/free/en/papers/yu-2019-cosql-conversational-text-sql-challenge/)** *(2019 · 261 citations)* CoSQL targets dialogue-based natural language interfaces to databases (NLIDB), where users explore a relational database across several turns rather than asking one well-formed question. 5. **[OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization](https://md.research.elamur.ai/free/en/papers/iyer-2022-opt-iml-scaling-language-model/)** *(2022 · 237 citations)* Instruction-tuning means fine-tuning a large language model on many tasks phrased as instructions. It is known to help zero- and few-shot transfer, but it was unclear how individual design choices affect results. The authors examine these choices at scale. They merged 8 existing task collections into OPT-IML Bench… 6. **[FeTaQA: Free-form Table Question Answering](https://md.research.elamur.ai/free/en/papers/nan-2022-fetaqa-free-form-table-question/)** *(2022 · 234 citations)* Most table question answering benchmarks expect a short entity or value as the answer. Real users, however, often ask questions that require combining several facts from a table into a full sentence. To address this gap, the authors built FeTaQA, a dataset of 10,330 Wikipedia table questions with free-form answers.… 7. **[SParC: Cross-Domain Semantic Parsing in Context](https://md.research.elamur.ai/free/en/papers/yu-2019-sparc-cross-domain-semantic-parsing/)** *(2019 · 227 citations)* Most text-to-SQL research treats each question in isolation. Real users, however, tend to ask chains of related questions, where later turns leave out, point back to, or adjust earlier constraints. The main existing benchmark for this setting, ATIS, covers only flight booking with a single database. The authors fill… 8. **[Lifting the Curse of Multilinguality by Pre-training Modular Transformers](https://md.research.elamur.ai/free/en/papers/pfeiffer-2022-lifting-curse-multilinguality-pre-training/)** *(2022 · 172 citations)* Massively multilingual encoders such as mBERT and XLM-R suffer from the "curse of multilinguality": when model capacity is fixed, adding more pre-training languages eventually lowers quality for each language. 9. **[RA-DIT: Retrieval-Augmented Dual Instruction Tuning](https://md.research.elamur.ai/free/en/papers/lin-2023-ra-dit-retrieval-augmented-dual/)** *(2023 · 145 citations)* Large language models store knowledge only in their weights, so they handle rare facts poorly and are costly to update. 10. **[LEVER: Learning to Verify Language-to-Code Generation with Execution](https://md.research.elamur.ai/free/en/papers/ni-2023-lever-learning-verify-language-code/)** *(2023 · 122 citations)* Code-oriented large language models can turn natural-language requests into programs, but their single best guess is often wrong. ## 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 12 of 54 co-authors with a page, the most papers together first. - [Mikel Artetxe](https://md.research.elamur.ai/free/en/authors/mikel-artetxe/) ×3 - [Naman Goyal](https://md.research.elamur.ai/free/en/authors/naman-goyal/) ×3 - [Rui Zhang](https://md.research.elamur.ai/free/en/authors/rui-zhang/) ×3 - [Todor Mihaylov](https://md.research.elamur.ai/free/en/authors/todor-mihaylov/) ×3 - [Xian Li](https://md.research.elamur.ai/free/en/authors/xian-li/) ×3 - [Bo Pang](https://md.research.elamur.ai/free/en/authors/bo-pang/) ×2 - [Daniel Simig](https://md.research.elamur.ai/free/en/authors/daniel-simig/) ×2 - [Heyang Er](https://md.research.elamur.ai/free/en/authors/heyang-er/) ×2 - [Michihiro Yasunaga](https://md.research.elamur.ai/free/en/authors/michihiro-yasunaga/) ×2 - [Myle Ott](https://md.research.elamur.ai/free/en/authors/myle-ott/) ×2 - [Ramakanth Pasunuru](https://md.research.elamur.ai/free/en/authors/ramakanth-pasunuru/) ×2 - [Shuohui Chen](https://md.research.elamur.ai/free/en/authors/shuohui-chen/) ×2 ## Cited by Papers of our library cite their works 3,430 times (of the 140,884 papers whose bibliographies we read); [249 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/xi-victoria-lin/cited-by/).