Swaroop Mishra

3.7k total citations · 2 hit papers
29 papers, 787 citations indexed

About

Swaroop Mishra is a scholar working on Artificial Intelligence, Control and Systems Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Swaroop Mishra has authored 29 papers receiving a total of 787 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 6 papers in Control and Systems Engineering and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Swaroop Mishra's work include Topic Modeling (14 papers), Natural Language Processing Techniques (13 papers) and Microgrid Control and Optimization (5 papers). Swaroop Mishra is often cited by papers focused on Topic Modeling (14 papers), Natural Language Processing Techniques (13 papers) and Microgrid Control and Optimization (5 papers). Swaroop Mishra collaborates with scholars based in United States, India and United Kingdom. Swaroop Mishra's co-authors include Hannaneh Hajishirzi, Daniel Khashabi, Chitta Baral, Alisa Liu, Yeganeh Kordi, Yi‐Zhong Wang, Noah A. Smith, Neeraj Varshney, Yejin Choi and Laxmidhar Behera and has published in prestigious journals such as IET Cyber-Physical Systems Theory & Applications, arXiv (Cornell University) and Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

In The Last Decade

Swaroop Mishra

26 papers receiving 751 citations

Hit Papers

Self-Instruct: Aligning Language Models with Self-Generat... 2022 2026 2023 2024 2023 2022 50 100 150 200 250

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Swaroop Mishra United States 14 613 173 94 47 46 29 787
Kartik Talamadupula United States 16 636 1.0× 147 0.8× 73 0.8× 9 0.2× 56 1.2× 55 841
Oscar Sainz Spain 4 432 0.7× 66 0.4× 114 1.2× 27 0.6× 16 0.3× 8 686
Hayley Ross United States 2 344 0.6× 54 0.3× 102 1.1× 27 0.6× 15 0.3× 4 587
James Wexler United States 7 409 0.7× 211 1.2× 60 0.6× 18 0.4× 8 0.2× 13 641
Zhuosheng Zhang China 20 1.3k 2.1× 365 2.1× 125 1.3× 26 0.6× 12 0.3× 73 1.5k
Marjan Ghazvininejad United States 15 1.1k 1.8× 421 2.4× 78 0.8× 22 0.5× 17 0.4× 32 1.3k
Stella Biderman United States 7 565 0.9× 98 0.6× 119 1.3× 9 0.2× 10 0.2× 15 720
Chengyu Wang China 15 570 0.9× 140 0.8× 103 1.1× 16 0.3× 9 0.2× 101 771
Damai Dai China 10 359 0.6× 125 0.7× 67 0.7× 14 0.3× 11 0.2× 18 530
Daniel Khashabi United States 20 1.3k 2.1× 432 2.5× 152 1.6× 28 0.6× 7 0.2× 46 1.5k

Countries citing papers authored by Swaroop Mishra

Since Specialization
Citations

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

Fields of papers citing papers by Swaroop Mishra

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Swaroop Mishra

This figure shows the co-authorship network connecting the top 25 collaborators of Swaroop Mishra. A scholar is included among the top collaborators of Swaroop Mishra 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 Swaroop Mishra. Swaroop Mishra 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.
2.
Gupta, Himanshu, et al.. (2024). InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis. 720–736. 13 indexed citations
3.
Mishra, Swaroop, et al.. (2023). HELP ME THINK: A Simple Prompting Strategy for Non-experts to Create Customized Content with Models. 11834–11890. 9 indexed citations
4.
Mishra, Swaroop, et al.. (2023). InstructExcel: A Benchmark for Natural Language Instruction in Excel. 4026–4043.
5.
Parmar, Mihir, Swaroop Mishra, Mor Geva, & Chitta Baral. (2023). Don’t Blame the Annotator: Bias Already Starts in the Annotation Instructions. 1779–1789. 22 indexed citations
6.
Wang, Yi‐Zhong, Yeganeh Kordi, Swaroop Mishra, et al.. (2023). Self-Instruct: Aligning Language Models with Self-Generated Instructions. 13484–13508. 269 indexed citations breakdown →
7.
Gupta, Himanshu, Neeraj Varshney, Swaroop Mishra, et al.. (2023). “John is 50 years old, can his son be 65?” Evaluating NLP Models’ Understanding of Feasibility. 3 indexed citations
8.
Mishra, Swaroop, Daniel Khashabi, Chitta Baral, & Hannaneh Hajishirzi. (2022). Cross-Task Generalization via Natural Language Crowdsourcing Instructions. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 3470–3487. 167 indexed citations breakdown →
9.
Mishra, Swaroop, Daniel Khashabi, Chitta Baral, Yejin Choi, & Hannaneh Hajishirzi. (2022). Reframing Instructional Prompts to GPTk’s Language. Findings of the Association for Computational Linguistics: ACL 2022. 589–612. 73 indexed citations
10.
Mishra, Swaroop, et al.. (2022). Is a Question Decomposition Unit All We Need?. 4553–4569. 13 indexed citations
11.
Mishra, Swaroop, Matthew Finlayson, Pan Lu, et al.. (2022). LILA: A Unified Benchmark for Mathematical Reasoning. 5807–5832. 25 indexed citations
12.
Mishra, Swaroop, et al.. (2022). Towards the Development of Disaster Management Tailored Machine Learning Systems. 1–6. 2 indexed citations
13.
Varshney, Neeraj, Swaroop Mishra, & Chitta Baral. (2022). ILDAE: Instance-Level Difficulty Analysis of Evaluation Data. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 3412–3425. 10 indexed citations
14.
Mishra, Swaroop, Arindam Mitra, Neeraj Varshney, et al.. (2022). NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 3505–3523. 29 indexed citations
15.
Mishra, Swaroop, Daniel Khashabi, Chitta Baral, & Hannaneh Hajishirzi. (2021). Natural Instructions: Benchmarking Generalization to New Tasks from Natural Language Instructions. arXiv (Cornell University). 18 indexed citations
16.
Banerjee, Pratyay, Swaroop Mishra, Kuntal Kumar Pal, Arindam Mitra, & Chitta Baral. (2021). Commonsense Reasoning with Implicit Knowledge in Natural Language. 3 indexed citations
17.
Mishra, Swaroop, et al.. (2021). How Robust are Model Rankings : A Leaderboard Customization Approach for Equitable Evaluation. Proceedings of the AAAI Conference on Artificial Intelligence. 35(15). 13561–13569. 9 indexed citations
18.
Mitra, Arindam, Pratyay Banerjee, Kuntal Kumar Pal, Swaroop Mishra, & Chitta Baral. (2019). Exploring ways to incorporate additional knowledge to improve Natural Language Commonsense Question Answering. arXiv (Cornell University). 19 indexed citations
19.
Mishra, Swaroop, et al.. (2017). Hybrid adaptive framework for coordinated control of distributed generators in cyber‐physical energy systems. IET Cyber-Physical Systems Theory & Applications. 3(1). 54–62. 13 indexed citations
20.
Mishra, Swaroop, et al.. (2016). Improving microgrid voltage stability through cyber-physical control. 1–6. 6 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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