Liang Pang

4.0k total citations
107 papers, 1.9k citations indexed

About

Liang Pang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering. According to data from OpenAlex, Liang Pang has authored 107 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 53 papers in Artificial Intelligence, 25 papers in Computer Vision and Pattern Recognition and 12 papers in Electrical and Electronic Engineering. Recurrent topics in Liang Pang's work include Topic Modeling (38 papers), Natural Language Processing Techniques (22 papers) and Multimodal Machine Learning Applications (14 papers). Liang Pang is often cited by papers focused on Topic Modeling (38 papers), Natural Language Processing Techniques (22 papers) and Multimodal Machine Learning Applications (14 papers). Liang Pang collaborates with scholars based in China, United States and Singapore. Liang Pang's co-authors include Xueqi Cheng, Yanyan Lan, Jiafeng Guo, Jun Xu, Shengxian Wan, Jing Qin, Jianxin Wang, Jianming Liang, Ruixiang Li and Qingyao Ai and has published in prestigious journals such as Angewandte Chemie International Edition, IEEE Transactions on Pattern Analysis and Machine Intelligence and Carbon.

In The Last Decade

Liang Pang

96 papers receiving 1.9k citations

Peers

Liang Pang
Weipeng Cao China
Xinyu Dai China
Rong Gu China
Yi Shen China
Mingyu Chen China
Junsheng Zhang China
Xiyu Liu China
Xiaoke Ma China
Shubo Liu China
Weipeng Cao China
Liang Pang
Citations per year, relative to Liang Pang Liang Pang (= 1×) peers Weipeng Cao

Countries citing papers authored by Liang Pang

Since Specialization
Citations

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

Fields of papers citing papers by Liang Pang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Liang Pang

This figure shows the co-authorship network connecting the top 25 collaborators of Liang Pang. A scholar is included among the top collaborators of Liang Pang 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 Liang Pang. Liang Pang 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.
Dai, Sunhao, et al.. (2025). Exploring the Escalation of Source Bias in User, Data, and Recommender System Feedback Loop. 1676–1686. 1 indexed citations
2.
Deng, Yang, Moxin Li, Liang Pang, Wenxuan Zhang, & Wai Lam. (2025). Unveiling Knowledge Boundary of Large Language Models for Trustworthy Information Access. 4086–4089.
3.
Dai, Sunhao, et al.. (2025). Unifying Bias and Unfairness in Information Retrieval: New Challenges in the LLM Era. 998–1001. 3 indexed citations
4.
Dai, Sunhao, et al.. (2025). Mitigating Source Bias with LLM Alignment. 370–380. 1 indexed citations
5.
Chen, Xu, Wenjie Wang, Liang Pang, et al.. (2025). Understanding Accuracy-Fairness Trade-offs in Re-ranking through Elasticity in Economics. UvA-DARE (University of Amsterdam). 539–548.
6.
Wang, Wenjie, et al.. (2024). A Study of Implicit Ranking Unfairness in Large Language Models. 7957–7970. 3 indexed citations
7.
Pang, Liang, Mo Yu, Fandong Meng, et al.. (2024). Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation. 133–145. 3 indexed citations
8.
Pang, Liang, et al.. (2024). Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Images. 208–217. 6 indexed citations
9.
10.
Dai, Sunhao, et al.. (2024). Neural Retrievers are Biased Towards LLM-Generated Content. 526–537. 10 indexed citations
11.
Chen, Xu, et al.. (2024). A Taxation Perspective for Fair Re-ranking. 1494–1503. 4 indexed citations
12.
13.
Pang, Liang, Yuan Yao, Wenqiao Zhang, et al.. (2024). Fact :Teaching MLLMs with <u>Fa</u>ithful, <u>C</u>oncise and <u>T</u>ransferable Rationales. 846–855. 1 indexed citations
14.
Liu, Pingsheng, Zhengjie Huang, Linlin Wang, et al.. (2023). A Disentangled-Attention Based Framework with Persona-Aware Prompt Learning for Dialogue Generation. Proceedings of the AAAI Conference on Artificial Intelligence. 37(11). 13255–13263. 2 indexed citations
15.
16.
Yu, Weijie, Xu Chen, Jun Xu, Liang Pang, & Ji-Rong Wen. (2022). Distribution Distance Regularized Sequence Representation for Text Matching in Asymmetrical Domains. IEEE/ACM Transactions on Audio Speech and Language Processing. 30. 721–733. 3 indexed citations
17.
Wang, Zhengbin, Liang Pang, & Y. G. Zheng. (2022). A review on under-deposit corrosion of pipelines in oil and gas fields: Testing methods, corrosion mechanisms and mitigation strategies. Corrosion Communications. 7. 70–81. 36 indexed citations
18.
Li, Ruixiang, Zhiqing Pang, Huining He, et al.. (2017). Drug depot-anchoring hydrogel: A self-assembling scaffold for localized drug release and enhanced stem cell differentiation. Journal of Controlled Release. 261. 234–245. 21 indexed citations
19.
Liu, Defu, et al.. (2008). Design Code Calibration of Coastal Defences Against Typhoon Attacks For Nuclear Power Plant. 1 indexed citations
20.
Liu, Defu, et al.. (2006). Joint Probability Analysis of Hurricane Katrina 2005. 9 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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