Adina Williams

6.3k total citations · 1 hit paper
32 papers, 971 citations indexed

About

Adina Williams is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Cultural Studies. According to data from OpenAlex, Adina Williams has authored 32 papers receiving a total of 971 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Artificial Intelligence, 9 papers in Computer Vision and Pattern Recognition and 4 papers in Cultural Studies. Recurrent topics in Adina Williams's work include Topic Modeling (22 papers), Natural Language Processing Techniques (20 papers) and Multimodal Machine Learning Applications (9 papers). Adina Williams is often cited by papers focused on Topic Modeling (22 papers), Natural Language Processing Techniques (20 papers) and Multimodal Machine Learning Applications (9 papers). Adina Williams collaborates with scholars based in United States, Israel and Canada. Adina Williams's co-authors include Alexis Conneau, Guillaume Lample, Ruty Rinott, Veselin Stoyanov, Holger Schwenk, Samuel Bowman, Douwe Kiela, Samuel R. Bowman, Candace Ross and Dieuwke Hupkes and has published in prestigious journals such as Science Advances, Neuropsychologia and Personality and Social Psychology Bulletin.

In The Last Decade

Adina Williams

29 papers receiving 907 citations

Hit Papers

XNLI: Evaluating Cross-lingual Sentence Representations 2018 2026 2020 2023 2018 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. XNLI: Evaluating Cross-lingual Sentence Representations
    2018 499 citations Alexis Conneau, Ruty Rinott et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Adina Williams United States 11 849 284 46 44 32 32 971
Emiel van Miltenburg Netherlands 11 386 0.5× 133 0.5× 40 0.9× 26 0.6× 15 0.5× 29 494
Jena D. Hwang United States 14 580 0.7× 65 0.2× 38 0.8× 18 0.4× 23 0.7× 48 676
Reut Tsarfaty Israel 15 1.1k 1.3× 122 0.4× 66 1.4× 21 0.5× 13 0.4× 57 1.2k
Gemma Boleda Spain 16 818 1.0× 115 0.4× 38 0.8× 42 1.0× 15 0.5× 54 995
Annie Louis United States 16 960 1.1× 91 0.3× 128 2.8× 13 0.3× 32 1.0× 53 1.1k
Hrafn Loftsson Iceland 11 497 0.6× 35 0.1× 58 1.3× 19 0.4× 19 0.6× 39 641
Joseph Mariani France 9 479 0.6× 50 0.2× 44 1.0× 23 0.5× 24 0.8× 52 622
Raquel Hervás Spain 9 230 0.3× 59 0.2× 23 0.5× 18 0.4× 39 1.2× 53 338
Tuhin Chakrabarty United States 13 337 0.4× 97 0.3× 47 1.0× 9 0.2× 26 0.8× 30 433
Jan Odijk Netherlands 8 449 0.5× 34 0.1× 54 1.2× 21 0.5× 20 0.6× 41 592

Countries citing papers authored by Adina Williams

Since Specialization
Citations

This map shows the geographic impact of Adina Williams's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Adina Williams with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Adina Williams more than expected).

Fields of papers citing papers by Adina Williams

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Adina Williams. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Adina Williams. The network helps show where Adina Williams may publish in the future.

Co-authorship network of co-authors of Adina Williams

This figure shows the co-authorship network connecting the top 25 collaborators of Adina Williams. A scholar is included among the top collaborators of Adina Williams based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Adina Williams. Adina Williams is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Wilcox, Ethan, Michael Y. Hu, Aaron Mueller, et al.. (2025). Bigger is not always better: The importance of human-scale language modeling for psycholinguistics. Journal of Memory and Language. 144. 104650–104650.
2.
3.
Williams, Adina, et al.. (2023). A Latent-Variable Model for Intrinsic Probing. Proceedings of the AAAI Conference on Artificial Intelligence. 37(11). 13591–13599. 1 indexed citations
4.
Sinha, Koustuv, Jon Gauthier, Aaron Mueller, et al.. (2023). Language model acceptability judgements are not always robust to context. 6043–6063. 6 indexed citations
5.
Hosseini, Saghar, et al.. (2023). ROBBIE: Robust Bias Evaluation of Large Generative Language Models. 3764–3814. 6 indexed citations
6.
Dessì, Roberto, et al.. (2023). Robustness of Named-Entity Replacements for In-Context Learning. 10914–10931. 2 indexed citations
7.
Williams, Adina, et al.. (2023). The Validity of Evaluation Results: Assessing Concurrence Across Compositionality Benchmarks. 274–293. 2 indexed citations
8.
9.
Ross, Candace, et al.. (2022). Perturbation Augmentation for Fairer NLP. 9496–9521. 29 indexed citations
10.
Thrush, Tristan, Max Bartolo, Amanpreet Singh, et al.. (2022). Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 5228–5238. 98 indexed citations
11.
Bailey, April H., Adina Williams, & Andrei Cimpian. (2022). Based on billions of words on the internet, people = men. Science Advances. 8(13). eabm2463–eabm2463. 29 indexed citations
12.
Talat, Zeerak, et al.. (2022). On the Machine Learning of Ethical Judgments from Natural Language. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 769–779. 19 indexed citations
13.
Williams, Adina, et al.. (2022). Investigating Failures of Automatic Translationin the Case of Unambiguous Gender. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 3454–3469. 8 indexed citations
14.
Sinha, Koustuv, et al.. (2021). Sometimes We Want Ungrammatical Translations. Empirical Methods in Natural Language Processing. 3205–3227. 1 indexed citations
15.
Sinha, Koustuv, Robin Jia, Dieuwke Hupkes, et al.. (2021). Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2888–2913. 88 indexed citations
16.
Dankers, Verna, et al.. (2021). Generalising to German Plural Noun Classes, from the Perspective of a Recurrent Neural Network. 94–108. 8 indexed citations
17.
Williams, Adina, et al.. (2020). Intrinsic Probing through Dimension Selection. Repository for Publications and Research Data (ETH Zurich). 23 indexed citations
18.
Conneau, Alexis, Ruty Rinott, Guillaume Lample, et al.. (2018). XNLI: Evaluating Cross-lingual Sentence Representations. 2475–2485. 499 indexed citations breakdown →
19.
Williams, Adina, Andrew Drozdov, & Samuel R. Bowman. (2017). Learning to parse from a semantic objective: It works. Is it syntax?. arXiv (Cornell University). 8 indexed citations
20.
Williams, Adina, et al.. (2017). Early sensitivity of left perisylvian cortex to relationality in nouns and verbs. Neuropsychologia. 100. 131–143. 27 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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