Sarah Wiegreffe

594 total citations
11 papers, 160 citations indexed

About

Sarah Wiegreffe is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems and Management. According to data from OpenAlex, Sarah Wiegreffe has authored 11 papers receiving a total of 160 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 1 paper in Computer Vision and Pattern Recognition and 1 paper in Information Systems and Management. Recurrent topics in Sarah Wiegreffe's work include Topic Modeling (8 papers), Explainable Artificial Intelligence (XAI) (6 papers) and Natural Language Processing Techniques (5 papers). Sarah Wiegreffe is often cited by papers focused on Topic Modeling (8 papers), Explainable Artificial Intelligence (XAI) (6 papers) and Natural Language Processing Techniques (5 papers). Sarah Wiegreffe collaborates with scholars based in United States. Sarah Wiegreffe's co-authors include Ana Marasović, Mark Riedl, Yejin Choi, Jack Hessel, Swabha Swayamdipta, Siyan Li, Xiang Li, Wenlong Zhao, Ashish Sabharwal and Mohit Bansal and has published in prestigious journals such as arXiv (Cornell University) and Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.

In The Last Decade

Sarah Wiegreffe

9 papers receiving 153 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sarah Wiegreffe United States	5	147	19	12	11	8	11	160
Jasmijn Bastings United States	5	164 1.1×	37 1.9×	15 1.3×	8 0.7×	8 1.0×	8	195
Potsawee Manakul United Kingdom	5	130 0.9×	9 0.5×	17 1.4×	7 0.6×	4 0.5×	13	173
Oana-Maria Camburu United Kingdom	5	212 1.4×	57 3.0×	14 1.2×	10 0.9×	5 0.6×	15	227
Piyawat Lertvittayakumjorn United Kingdom	6	105 0.7×	21 1.1×	14 1.2×	4 0.4×	6 0.8×	19	127
Alex Marin United States	9	184 1.3×	19 1.0×	24 2.0×	2 0.2×	5 0.6×	20	208
Jiaao Chen United States	5	90 0.6×	18 0.9×	9 0.8×	2 0.2×	8 1.0×	9	114
Daniel Deutsch United States	9	211 1.4×	35 1.8×	23 1.9×	4 0.4×	9 1.1×	31	226
Laure Soulier France	7	62 0.4×	21 1.1×	25 2.1×	7 0.6×	6 0.8×	25	93
Daniel Bär Germany	5	225 1.5×	21 1.1×	20 1.7×	4 0.4×	23 2.9×	9	242
Bartosz Broda Poland	9	187 1.3×	13 0.7×	16 1.3×	3 0.3×	8 1.0×	23	209

Countries citing papers authored by Sarah Wiegreffe

Since Specialization

Citations

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

Fields of papers citing papers by Sarah Wiegreffe

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sarah Wiegreffe

This figure shows the co-authorship network connecting the top 25 collaborators of Sarah Wiegreffe. A scholar is included among the top collaborators of Sarah Wiegreffe 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 Sarah Wiegreffe. Sarah Wiegreffe 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:

11 of 11 papers shown

1.

Hase, Peter, Mohit Bansal, Peter E. Clark, & Sarah Wiegreffe. (2024). The Unreasonable Effectiveness of Easy Training Data for Hard Tasks. 7002–7024.

2.

Wiegreffe, Sarah, et al.. (2024). Mechanistic?. 480–498. 1 indexed citations

3.

Zhao, Wenlong, et al.. (2023). Editing Common Sense in Transformers. 8214–8232. 3 indexed citations

4.

Wiegreffe, Sarah, Matthew Finlayson, Oyvind Tafjord, Peter E. Clark, & Ashish Sabharwal. (2023). Increasing Probability Mass on Answer Choices Does Not Always Improve Accuracy. 8392–8417.

5.

Gupta, Prakhar, et al.. (2023). Self-Refine: Iterative Refinement with Self-Feedback. 46534–46594. 1 indexed citations

6.

Wiegreffe, Sarah, et al.. (2022). Calibrating Trust of Multi-Hop Question Answering Systems with Decompositional Probes. 2888–2902. 4 indexed citations

7.

Li, Siyan, et al.. (2022). Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning. 7008–7029. 13 indexed citations

8.

Wiegreffe, Sarah, Jack Hessel, Swabha Swayamdipta, Mark Riedl, & Yejin Choi. (2022). Reframing Human-AI Collaboration for Generating Free-Text Explanations. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 632–658. 60 indexed citations

9.

Wiegreffe, Sarah & Ana Marasović. (2021). Teach Me to Explain: A Review of Datasets for Explainable NLP.. arXiv (Cornell University). 31 indexed citations

10.

Wiegreffe, Sarah & Ana Marasović. (2021). Teach Me to Explain: A Review of Datasets for Explainable Natural\n Language Processing. arXiv (Cornell University). 37 indexed citations

11.

Wiegreffe, Sarah & Ana Marasović. (2021). Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing. arXiv (Cornell University). 10 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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