--- title: "Julian Eisenschlos — author profile and papers" description: "Julian Eisenschlos: 2 papers on Elamur Research (2020–2022), h-index 15 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/julian-eisenschlos/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/julian-eisenschlos/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Julian Eisenschlos # Julian Eisenschlos Julian Eisenschlos works in natural language processing, studying how pretrained language models can reason over structured inputs and stored facts. Their papers here belong to [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) and [Semantic parsing & meaning representation](https://md.research.elamur.ai/free/en/topics/semantic-parsing/), with a link to knowledge graphs. A recurring question is what a model can draw from material other than plain running text, such as tables or facts that hold only during certain years. The usual approach is to adapt transformer encoders and their pretraining, then test them on benchmarks built for the purpose. The most cited of these papers is [TaPas: Weakly Supervised Table Parsing via Pre-training](https://md.research.elamur.ai/free/en/papers/herzig-2020-tapas-weakly-supervised-table-parsing/) (2020). It answers questions about tables without first translating them into executable logical forms. Instead, it extends BERT to read a question together with a flattened table, adding embeddings that mark each token's row, column and numeric rank. The model then picks cells and an aggregation operator, and it learns from answers alone. The other paper, [Time-Aware Language Models as Temporal Knowledge Bases](https://md.research.elamur.ai/free/en/papers/dhingra-2022-time-aware-language-models-as/) (2022), examines what happens when a model trained on one snapshot of text meets facts that change over time. It introduces TempLAMA, a collection of fill-in-the-blank queries drawn from Wikidata, where the right answer varies with the year. The study finds that the model blends statements that were true in different years, loses track of years with little coverage, and makes poorly calibrated guesses about what comes later. The 2022 paper marks a shift from question answering over tables toward the temporal side of factual knowledge in language models. Co-authors on these papers include [Jonathan Herzig](https://md.research.elamur.ai/free/en/authors/jonathan-herzig/), [Bhuwan Dhingra](https://md.research.elamur.ai/free/en/authors/bhuwan-dhingra/) and [William Cohen](https://md.research.elamur.ai/free/en/authors/william-cohen/), along with Jacob Eisenstein and Daniel Gillick. Eisenschlos's bylines run from 2019 to 2025. The most recent ones name [Google DeepMind](https://md.research.elamur.ai/free/en/companies/google-deepmind/) in London, and earlier ones name [Google](https://md.research.elamur.ai/free/en/companies/google/) in Mountain View and in Zurich. Some bylines also list the [University of Cambridge](https://md.research.elamur.ai/free/en/universities/university-of-cambridge/) and the Universidad Nacional de Córdoba. Julian Eisenschlos has 2 papers in our library, in 2020–2022, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Knowledge graphs & ontologies](https://md.research.elamur.ai/free/en/topics/knowledge-graphs/), [Semantic parsing & meaning representation](https://md.research.elamur.ai/free/en/topics/semantic-parsing/); often with [Bhuwan Dhingra](https://md.research.elamur.ai/free/en/authors/bhuwan-dhingra/), [Daniel Gillick](https://md.research.elamur.ai/free/en/authors/daniel-gillick/), [Jacob Eisenstein](https://md.research.elamur.ai/free/en/authors/jacob-eisenstein/). 2 papers in our library · 2020–2022 [OpenAlex](https://openalex.org/A5000738730) · [**h-index 15**](https://openalex.org/authors/A5000738730) · [71 works](https://openalex.org/works?filter=author.id:A5000738730) · [2,204 citations](https://openalex.org/works?filter=author.id:A5000738730&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/julian-eisenschlos/#papers) · [🔗 3,183 citations in our library](https://md.research.elamur.ai/free/en/authors/julian-eisenschlos/cited-by/) · [8 more in our library without a page yet](#also) · [⭐ Paper Star rank #1940](https://md.research.elamur.ai/free/en/paper-stars/page/20/#star-1940) 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=Julian+Eisenschlos) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Julian+Eisenschlos) · [dblp](https://dblp.org/search?q=Julian+Eisenschlos) · [ACL Anthology](https://aclanthology.org/search/?q=Julian+Eisenschlos) ## Affiliations [🏢**Google DeepMind** London · United Kingdom 2021–2025 · 23 papers](https://md.research.elamur.ai/free/en/companies/google-deepmind/) · [🏢**Google** Mountain View · United States 2019–2024 · 19 papers](https://md.research.elamur.ai/free/en/companies/google/) · [🏢**Google** Zurich · Switzerland 2020–2022 · 7 papers](https://md.research.elamur.ai/free/en/companies/google-switzerland/) · [🎓**University of Cambridge** Cambridge · United Kingdom 2022–2023 · 4 papers](https://md.research.elamur.ai/free/en/universities/university-of-cambridge/) · [🎓**Universidad Nacional de Córdoba** Córdoba · Argentina 2024–2025 · 3 papers](https://ror.org/056tb7j80) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 more institutions on single bylines are not listed. ## Topics [Language models & pretraining (2)](https://md.research.elamur.ai/free/en/topics/language-models/) · [Knowledge graphs & ontologies (1)](https://md.research.elamur.ai/free/en/topics/knowledge-graphs/) · [Semantic parsing & meaning representation (1)](https://md.research.elamur.ai/free/en/topics/semantic-parsing/) ## Works by year ### 2020s [**2022** 1 paper](https://md.research.elamur.ai/free/en/papers/dhingra-2022-time-aware-language-models-as/) · [**2020** 1 paper](https://md.research.elamur.ai/free/en/papers/herzig-2020-tapas-weakly-supervised-table-parsing/) ## Papers The most cited first (OpenAlex and our library). 1. **[TaPas: Weakly Supervised Table Parsing via Pre-training](https://md.research.elamur.ai/free/en/papers/herzig-2020-tapas-weakly-supervised-table-parsing/)** *(2020 · 844 citations)* Answering questions over tables is usually treated as semantic parsing: the question is converted into an executable logical form. 2. **[Time-Aware Language Models as Temporal Knowledge Bases](https://md.research.elamur.ai/free/en/papers/dhingra-2022-time-aware-language-models-as/)** *(2022 · 194 citations)* Language models are often viewed as repositories of facts. However, many facts become outdated over time: someone changes club, position, or employer. Models, meanwhile, are trained on a snapshot of data collected at a particular moment. The authors from Google Research investigate how this affects the knowledge of… ## Also in our library 8 more works of theirs are in our library without a page here yet; they will appear as we publish them. ## Co-authors here 5 co-authors with a page, the most papers together first. - [Bhuwan Dhingra](https://md.research.elamur.ai/free/en/authors/bhuwan-dhingra/) - [Daniel Gillick](https://md.research.elamur.ai/free/en/authors/daniel-gillick/) - [Jacob Eisenstein](https://md.research.elamur.ai/free/en/authors/jacob-eisenstein/) - [Jonathan Herzig](https://md.research.elamur.ai/free/en/authors/jonathan-herzig/) - [William Cohen](https://md.research.elamur.ai/free/en/authors/william-cohen/) ## Cited by Papers of our library cite their works 3,183 times (of the 140,884 papers whose bibliographies we read); [166 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/julian-eisenschlos/cited-by/).