Robin Jia

6.7k total citations · 2 hit papers
35 papers, 2.5k citations indexed

About

Robin Jia is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Robin Jia has authored 35 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Artificial Intelligence, 10 papers in Computer Vision and Pattern Recognition and 4 papers in Information Systems. Recurrent topics in Robin Jia's work include Topic Modeling (26 papers), Natural Language Processing Techniques (21 papers) and Multimodal Machine Learning Applications (9 papers). Robin Jia is often cited by papers focused on Topic Modeling (26 papers), Natural Language Processing Techniques (21 papers) and Multimodal Machine Learning Applications (9 papers). Robin Jia collaborates with scholars based in United States, Israel and France. Robin Jia's co-authors include Percy Liang, Pranav Rajpurkar, Aditi Raghunathan, Douwe Kiela, Eunsol Choi, Danqi Chen, Adam Fisch, Amir Marcovitz, Alon Talmor and Gill Bejerano and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Molecular Biology and Evolution and arXiv (Cornell University).

In The Last Decade

Robin Jia

31 papers receiving 2.3k 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.

Know What You Don’t Know: Unanswerable Questions for SQuAD

2018 992 citations Pranav Rajpurkar, Robin Jia et al. profile →
Adversarial Examples for Evaluating Reading Comprehension Systems

2017 663 citations Robin Jia, Percy Liang profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Robin Jia United States	12	2.3k	692	288	129	99	35	2.5k
Julian Michael United States	11	3.1k 1.4×	1.0k 1.5×	269 0.9×	80 0.6×	118 1.2×	17	3.5k
Alexis Conneau Israel	15	2.9k 1.3×	695 1.0×	270 0.9×	259 2.0×	85 0.9×	19	3.1k
Wanxiang Che China	36	3.9k 1.7×	762 1.1×	377 1.3×	185 1.4×	211 2.1×	178	4.3k
Jörg Tiedemann Sweden	32	3.9k 1.7×	622 0.9×	206 0.7×	65 0.5×	144 1.5×	176	4.2k
Jason Baldridge United States	30	2.2k 1.0×	842 1.2×	269 0.9×	200 1.6×	84 0.8×	89	3.0k
Ari Holtzman United States	12	1.1k 0.5×	662 1.0×	120 0.4×	53 0.4×	23 0.2×	19	1.6k
Alex Wang United States	10	2.2k 1.0×	665 1.0×	290 1.0×	82 0.6×	115 1.2×	19	3.2k
Chuanqi Tan China	22	2.0k 0.9×	267 0.4×	250 0.9×	52 0.4×	146 1.5×	43	2.2k
Sewon Min United States	15	1.1k 0.5×	347 0.5×	170 0.6×	42 0.3×	33 0.3×	29	1.3k
Kentaro Inui Japan	26	2.4k 1.0×	278 0.4×	430 1.5×	86 0.7×	77 0.8×	235	2.7k

Countries citing papers authored by Robin Jia

Since Specialization

Citations

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

Fields of papers citing papers by Robin Jia

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Robin Jia

This figure shows the co-authorship network connecting the top 25 collaborators of Robin Jia. A scholar is included among the top collaborators of Robin Jia 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 Robin Jia. Robin Jia 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.

Tak, Ala Nekouvaght, et al.. (2025). Mechanistic Interpretability of Emotion Inference in Large Language Models. 13090–13120. 1 indexed citations

2.

Wei, Johnny Tian-Zheng, et al.. (2025). Interrogating LLM design under copyright law. 3030–3045.

3.

Wei, Johnny Tian-Zheng, et al.. (2024). Proving membership in LLM pretraining data via data watermarks. 13306–13320. 6 indexed citations

4.

Wang, Zhu, Alekh Agarwal, Mandar Joshi, et al.. (2024). Efficient End-to-End Visual Document Understanding with Rationale Distillation. 8401–8424.

5.

Xu, Albert, Xiang Ren, & Robin Jia. (2023). Contrastive Novelty-Augmented Learning: Anticipating Outliers with Large Language Models. 11778–11801. 1 indexed citations

6.

Liu, Nelson F., Ananya Kumar, Percy Liang, & Robin Jia. (2023). Are Sample-Efficient NLP Models More Robust?. 1689–1709. 4 indexed citations

7.

Jia, Robin, et al.. (2023). Benchmarking Long-tail Generalization with Likelihood Splits. 963–983. 1 indexed citations

8.

Wang, Zhu, Jesse Thomason, & Robin Jia. (2023). Chain-of-Questions Training with Latent Answers for Robust Multistep Question Answering. 8845–8860.

9.

Yan, Jun, et al.. (2022). On the Robustness of Reading Comprehension Models to Entity Renaming. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 508–520. 2 indexed citations

10.

Lin, Bill, Sida Wang, Robin Jia, et al.. (2022). On Continual Model Refinement in Out-of-Distribution Data Streams. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 3128–3139. 8 indexed citations

11.

Bansal, Gagan, et al.. (2021). Do Explanations Help Users Detect Errors in Open-Domain QA? An Evaluation of Spoken vs. Visual Explanations. 1103–1116. 12 indexed citations

12.

Bartolo, Max, Tristan Thrush, Robin Jia, et al.. (2021). Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation. arXiv (Cornell University). 43 indexed citations

13.

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

14.

Mussmann, Stephen, Robin Jia, & Percy Liang. (2020). On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks. 3400–3413. 10 indexed citations

15.

Jia, Robin, et al.. (2019). Certified Robustness to Adversarial Word Substitutions. 4127–4140. 137 indexed citations

16.

Fisch, Adam, Alon Talmor, Robin Jia, et al.. (2019). MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension. 115 indexed citations

17.

Rajpurkar, Pranav, Robin Jia, & Percy Liang. (2018). Know What You Don’t Know: Unanswerable Questions for SQuAD. 784–789. 992 indexed citations breakdown →

18.

Jia, Robin & Percy Liang. (2017). Adversarial Examples for Evaluating Reading Comprehension Systems. 2021–2031. 663 indexed citations breakdown →

19.

Jia, Robin, et al.. (2017). Learning concepts through conversations in spoken dialogue systems. 50. 5725–5729. 2 indexed citations

20.

Jia, Robin & Percy Liang. (2016). Data Recombination for Neural Semantic Parsing. 12–22. 227 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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