Leshem Choshen

1.1k total citations
31 papers, 334 citations indexed

About

Leshem Choshen is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Cognitive Neuroscience. According to data from OpenAlex, Leshem Choshen has authored 31 papers receiving a total of 334 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 2 papers in Cognitive Neuroscience. Recurrent topics in Leshem Choshen's work include Topic Modeling (22 papers), Natural Language Processing Techniques (21 papers) and Multimodal Machine Learning Applications (6 papers). Leshem Choshen is often cited by papers focused on Topic Modeling (22 papers), Natural Language Processing Techniques (21 papers) and Multimodal Machine Learning Applications (6 papers). Leshem Choshen collaborates with scholars based in United States, Israel and Switzerland. Leshem Choshen's co-authors include Omri Abend, Noam Slonim, Eyal Shnarch, Ranit Aharonov, Lena Dankin, Roee Aharoni, Idan Szpektor, Or Honovich, Yoav Katz and Ariel Gera and has published in prestigious journals such as Journal of Memory and Language, Nature Machine Intelligence and Transactions of the Association for Computational Linguistics.

In The Last Decade

Leshem Choshen

23 papers receiving 305 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Leshem Choshen United States 8 298 42 41 13 9 31 334
Eric Nalisnick United States 7 173 0.6× 47 1.1× 42 1.0× 10 0.8× 9 1.0× 19 214
Alham Fikri Aji United Kingdom 10 388 1.3× 46 1.1× 79 1.9× 13 1.0× 8 0.9× 36 451
Stephan Gouws South Africa 8 449 1.5× 44 1.0× 70 1.7× 12 0.9× 12 1.3× 9 484
Dimitra Gkatzia United Kingdom 9 202 0.7× 28 0.7× 48 1.2× 12 0.9× 6 0.7× 33 248
David Uthus United States 8 191 0.6× 43 1.0× 31 0.8× 15 1.2× 7 0.8× 13 251
Yllias Chali Canada 13 447 1.5× 59 1.4× 60 1.5× 8 0.6× 14 1.6× 47 474
Niklas Muennighoff United States 6 302 1.0× 37 0.9× 55 1.3× 10 0.8× 9 1.0× 8 379
Shehzaad Dhuliawala United States 6 155 0.5× 24 0.6× 28 0.7× 8 0.6× 9 1.0× 14 192
Ryohei Sasano Japan 11 282 0.9× 42 1.0× 48 1.2× 8 0.6× 11 1.2× 50 320
Tomohide Shibata Japan 10 255 0.9× 87 2.1× 30 0.7× 16 1.2× 5 0.6× 29 280

Countries citing papers authored by Leshem Choshen

Since Specialization
Citations

This map shows the geographic impact of Leshem Choshen'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 Leshem Choshen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Leshem Choshen more than expected).

Fields of papers citing papers by Leshem Choshen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Leshem Choshen. 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 Leshem Choshen. The network helps show where Leshem Choshen may publish in the future.

Co-authorship network of co-authors of Leshem Choshen

This figure shows the co-authorship network connecting the top 25 collaborators of Leshem Choshen. A scholar is included among the top collaborators of Leshem Choshen 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 Leshem Choshen. Leshem Choshen 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.
Wood, Andrew W., et al.. (2025). ZipNN: Lossless Compression for AI Models. 186–198.
3.
Gera, Ariel, Liat Ein‐Dor, Eyal Shnarch, et al.. (2024). Efficient Benchmarking (of Language Models). 2519–2536. 4 indexed citations
4.
Choshen, Leshem, et al.. (2024). Fuse to Forget: Bias Reduction and Selective Memorization through Model Fusion. 18763–18783. 2 indexed citations
5.
Gera, Ariel, et al.. (2024). Label-Efficient Model Selection for Text Generation. 8384–8402.
6.
Choshen, Leshem, et al.. (2024). Holmes ⌕ A Benchmark to Assess the Linguistic Competence of Language Models. Transactions of the Association for Computational Linguistics. 12. 1616–1647.
7.
Akyürek, Ekin, et al.. (2024). Deductive Closure Training of Language Models for Coherence, Accuracy, and Updatability. 9802–9818. 2 indexed citations
8.
Schwartz, Eli, et al.. (2024). NumeroLogic: Number Encoding for Enhanced LLMs’ Numerical Reasoning. 206–212. 2 indexed citations
9.
Choshen, Leshem, et al.. (2023). Where to start? Analyzing the potential value of intermediate models. 1446–1470. 4 indexed citations
10.
Raffel, Colin, et al.. (2023). ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning. 788–806. 7 indexed citations
11.
Raffel, Colin, et al.. (2023). Knowledge is a Region in Weight Space for Fine-tuned Language Models. 1350–1370. 5 indexed citations
12.
Aharoni, Roee, et al.. (2023). DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answering. 10056–10070. 9 indexed citations
13.
Shnarch, Eyal, Alon Halfon, Ariel Gera, et al.. (2022). Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours. 159–168. 5 indexed citations
14.
15.
Choshen, Leshem, et al.. (2022). PreQuEL: Quality Estimation of Machine Translation Outputs in Advance. 11170–11183. 3 indexed citations
16.
Choshen, Leshem & Omri Abend. (2022). Enhancing the Transformer Decoder with Transition-based Syntax. 384–404. 1 indexed citations
17.
Honovich, Or, et al.. (2021). Q2: : Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 7856–7870. 66 indexed citations
18.
Ein‐Dor, Liat, Alon Halfon, Ariel Gera, et al.. (2020). Active Learning for BERT: An Empirical Study. 7949–7962. 79 indexed citations
19.
Choshen, Leshem, et al.. (2018). DORA The Explorer: Directed Outreaching Reinforcement Action-Selection. arXiv (Cornell University). 2018. 6 indexed citations
20.
Shnarch, Eyal, Carlos Alzate, Lena Dankin, et al.. (2018). Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining. 599–605. 31 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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