Alisa Liu

1.4k total citations · 1 hit paper
10 papers, 607 citations indexed

About

Alisa Liu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Communication. According to data from OpenAlex, Alisa Liu has authored 10 papers receiving a total of 607 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 2 papers in Computer Vision and Pattern Recognition and 1 paper in Communication. Recurrent topics in Alisa Liu's work include Natural Language Processing Techniques (8 papers), Topic Modeling (7 papers) and Multimodal Machine Learning Applications (2 papers). Alisa Liu is often cited by papers focused on Natural Language Processing Techniques (8 papers), Topic Modeling (7 papers) and Multimodal Machine Learning Applications (2 papers). Alisa Liu collaborates with scholars based in United States. Alisa Liu's co-authors include Noah A. Smith, Yejin Choi, Hannaneh Hajishirzi, Swaroop Mishra, Daniel Khashabi, Yeganeh Kordi, Yi‐Zhong Wang, Swabha Swayamdipta, Ximing Lu and Maarten Sap and has published in prestigious journals such as arXiv (Cornell University) and Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

In The Last Decade

Alisa Liu

9 papers receiving 576 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.

Self-Instruct: Aligning Language Models with Self-Generated Instructions

2023 269 citations Yi‐Zhong Wang, Yeganeh Kordi et al. profile →

Peers

Alisa Liu

AI

CVPR

IS

MSOR

HI

Swaroop Mishra United States

Teven Le Scao United States

Victor Sanh United States

Leo Gao Canada

Yutai Hou China

Jonathan Herzig Israel

Xinxi Lyu United States

Stella Biderman United States

Shuohuan Wang China

Canwen Xu United States

Swaroop Mishra United States

Alisa Liu

613 ×1.3

AI

173 ×1.5

CVPR

94 ×1.3

IS

27 ×0.9

MSOR

34 ×1.4

HI

Citations per year, relative to Alisa Liu Alisa Liu (= 1×) peers Swaroop Mishra

Countries citing papers authored by Alisa Liu

Since Specialization

Citations

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

Fields of papers citing papers by Alisa Liu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Alisa Liu

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

10 of 10 papers shown

1.

Yue, Xiang, et al.. (2025). Synthetic Data in the Era of Large Language Models. 11–12.

2.

Gonen, Hila, et al.. (2025). Does Liking Yellow Imply Driving a School Bus? Semantic Leakage in Language Models. 785–798. 1 indexed citations

3.

Liu, Alisa, Zhaofeng Wu, Julian Michael, et al.. (2023). We’re Afraid Language Models Aren’t Modeling Ambiguity. 790–807. 18 indexed citations

4.

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 →

5.

Liu, Alisa, et al.. (2023). Detoxifying Text with MaRCo: Controllable Revision with Experts and Anti-Experts. 228–242. 10 indexed citations

6.

Liu, Jiacheng, Alisa Liu, Ximing Lu, et al.. (2022). Generated Knowledge Prompting for Commonsense Reasoning. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 3154–3169. 108 indexed citations

7.

Liu, Alisa, Swabha Swayamdipta, Noah A. Smith, & Yejin Choi. (2022). WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation. 6826–6847. 82 indexed citations

8.

Liu, Alisa, Maarten Sap, Ximing Lu, et al.. (2021). On-the-Fly Controlled Text Generation with Experts and Anti-Experts.. arXiv (Cornell University). 5 indexed citations

9.

Liu, Alisa, Maarten Sap, Ximing Lu, et al.. (2021). DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts. 6691–6706. 94 indexed citations

10.

Chen, Michael, et al.. (2019). . 20 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.

Explore authors with similar magnitude of impact