Mohit Iyyer

7.2k total citations · 2 hit papers
62 papers, 2.1k citations indexed

About

Mohit Iyyer is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Mohit Iyyer has authored 62 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 56 papers in Artificial Intelligence, 13 papers in Computer Vision and Pattern Recognition and 5 papers in Information Systems. Recurrent topics in Mohit Iyyer's work include Topic Modeling (53 papers), Natural Language Processing Techniques (47 papers) and Multimodal Machine Learning Applications (11 papers). Mohit Iyyer is often cited by papers focused on Topic Modeling (53 papers), Natural Language Processing Techniques (47 papers) and Multimodal Machine Learning Applications (11 papers). Mohit Iyyer collaborates with scholars based in United States, India and Canada. Mohit Iyyer's co-authors include Jordan Boyd‐Graber, Hal Daumé, Varun Manjunatha, Luke Zettlemoyer, Eunsol Choi, Philip Resnik, Wen-tau Yih, Peter K. Enns, Yejin Choi and Mark Yatskar and has published in prestigious journals such as Language Resources and Evaluation, Transactions of the Association for Computational Linguistics and arXiv (Cornell University).

In The Last Decade

Mohit Iyyer

54 papers receiving 1.9k citations

Hit Papers

align trajectories

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.

Deep Unordered Composition Rivals Syntactic Methods for Text Classification

2015 448 citations Mohit Iyyer, Varun Manjunatha et al. profile →
QuAC: Question Answering in Context

2018 343 citations Eunsol Choi, Mohit Iyyer et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mohit Iyyer United States	19	1.8k	424	242	121	79	62	2.1k
Noah Constant United States	14	2.1k 1.2×	451 1.1×	275 1.1×	122 1.0×	40 0.5×	22	2.5k
Ellie Pavlick United States	23	2.5k 1.4×	540 1.3×	223 0.9×	89 0.7×	52 0.7×	72	2.9k
Dani Yogatama United States	17	1.5k 0.9×	205 0.5×	211 0.9×	93 0.8×	37 0.5×	30	1.8k
Samuel R. Bowman United States	16	3.2k 1.8×	893 2.1×	267 1.1×	118 1.0×	50 0.6×	40	3.4k
Kevin Gimpel United States	22	2.4k 1.4×	395 0.9×	302 1.2×	124 1.0×	35 0.4×	81	2.8k
Daniel Cer United States	26	3.6k 2.0×	641 1.5×	398 1.6×	139 1.1×	48 0.6×	42	4.0k
Ivan Vulić United Kingdom	30	2.5k 1.4×	513 1.2×	241 1.0×	43 0.4×	47 0.6×	157	2.8k
Nathanael Chambers United States	23	2.6k 1.4×	340 0.8×	321 1.3×	77 0.6×	54 0.7×	46	2.8k
Lucy Vanderwende United States	28	3.1k 1.7×	614 1.4×	441 1.8×	97 0.8×	45 0.6×	64	3.6k
Benjamin Van Durme United States	33	3.7k 2.1×	620 1.5×	569 2.4×	135 1.1×	49 0.6×	186	4.1k

Countries citing papers authored by Mohit Iyyer

Since Specialization

Citations

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

Fields of papers citing papers by Mohit Iyyer

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mohit Iyyer

This figure shows the co-authorship network connecting the top 25 collaborators of Mohit Iyyer. A scholar is included among the top collaborators of Mohit Iyyer 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 Mohit Iyyer. Mohit Iyyer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Le, Minh Nhat, et al.. (2025). OWL: Probing Cross-Lingual Recall of Memorized Texts via World Literature. 25879–25906.

2.

Lo, Kyle, et al.. (2024). One Thousand and One Pairs: A “novel” challenge for long-context language models. 17048–17085. 2 indexed citations

3.

Iyyer, Mohit, et al.. (2024). Triage of Messages and Conversations in a Large-Scale Child Victimization Corpus. 4544–4554.

4.

Vu, Tu, Mohit Iyyer, Xuezhi Wang, et al.. (2024). FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation. 13697–13720. 25 indexed citations

5.

Song, Yixiao, et al.. (2023). A Critical Evaluation of Evaluations for Long-form Question Answering. 3225–3245. 13 indexed citations

6.

Song, Yixiao, et al.. (2023). kNN-LM Does Not Improve Open-ended Text Generation. 15023–15037.

7.

Drozdov, Andrew, Honglei Zhuang, Zhuyun Dai, et al.. (2023). PaRaDe: Passage Ranking using Demonstrations with LLMs. 14242–14252. 4 indexed citations

8.

Yang, Qian, et al.. (2023). A Framework for Exploring Player Perceptions of LLM-Generated Dialogue in Commercial Video Games. 2295–2311. 10 indexed citations

9.

Min, Sewon, Kalpesh Krishna, Xinxi Lyu, et al.. (2023). FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation. 12076–12100. 80 indexed citations

10.

Krishna, Kalpesh, et al.. (2022). RankGen: Improving Text Generation with Large Ranking Models. 199–232. 18 indexed citations

11.

Xu, Fangyuan, et al.. (2022). Modeling Exemplification in Long-form Question Answering via Retrieval. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2079–2092. 5 indexed citations

12.

Manjunatha, Varun, et al.. (2021). TABBIE: Pretrained Representations of Tabular Data. 3446–3456. 63 indexed citations

13.

Sun, Simeng, Kalpesh Krishna, Andrew Mattarella-Micke, & Mohit Iyyer. (2021). Do Long-Range Language Models Actually Use Long-Range Context?. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 807–822. 19 indexed citations

14.

Iyyer, Mohit, et al.. (2021). Predicting in-hospital mortality by combining clinical notes with time-series data. 4026–4031. 22 indexed citations

15.

Vu, Tu, Tong Wang, Tsendsuren Munkhdalai, et al.. (2020). Exploring and Predicting Transferability across NLP Tasks. 7882–7926. 61 indexed citations

16.

Krishna, Kalpesh, Gaurav Singh Tomar, Ankur P. Parikh, Nicolas Papernot, & Mohit Iyyer. (2020). Thieves of Sesame Street: Model Extraction on BERT-based APIs. arXiv (Cornell University). 9 indexed citations

17.

Boyd‐Graber, Jordan, Fenfei Guo, Leah Findlater, & Mohit Iyyer. (2020). Which Evaluations Uncover Sense Representations that Actually Make Sense. Language Resources and Evaluation. 1727–1738. 1 indexed citations

18.

Iyyer, Mohit, Wen-tau Yih, & Ming‐Wei Chang. (2017). Search-based Neural Structured Learning for Sequential Question Answering. 1821–1831. 111 indexed citations

19.

Iyyer, Mohit, Varun Manjunatha, Yogarshi Vyas, et al.. (2017). The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives. 6478–6487. 42 indexed citations

20.

Iyyer, Mohit, Peter K. Enns, Jordan Boyd‐Graber, & Philip Resnik. (2014). Political Ideology Detection Using Recursive Neural Networks. 1113–1122. 174 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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