Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, et al.
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2022)
Open Access | Times Cited: 160

Showing 26-50 of 160 citing articles:

A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)
Rajvardhan Patil, And Venkat Gudivada
(2024)
Open Access | Times Cited: 15
EcomGPT: Instruction-Tuning Large Language Models with Chain-of-Task Tasks for E-commerce
Yangning Li, Shirong Ma, Xiaobin Wang, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2024) Vol. 38, Iss. 17, pp. 18582-18590
Open Access | Times Cited: 10
VIGC: Visual Instruction Generation and Correction
Bin Wang, Fan Wu, Xiao Han, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2024) Vol. 38, Iss. 6, pp. 5309-5317
Open Access | Times Cited: 9
Multitask Vision-Language Prompt Tuning
Sheng Shen, Shijia Yang, Tianjun Zhang, et al.
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (2024), pp. 5644-5655
Open Access | Times Cited: 8
Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Action
Jiasen Lu, Christopher M. Clark, Sang-Ho Lee, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024) Vol. 7, pp. 26429-26445
Closed Access | Times Cited: 8
Task-aware Retrieval with Instructions
Akari Asai, Timo Schick, Patrick Lewis, et al.
Findings of the Association for Computational Linguistics: ACL 2022 (2023)
Open Access | Times Cited: 21
One-Shot Labeling for Automatic Relevance Estimation
Sean MacAvaney, Luca Soldaini
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (2023), pp. 2230-2235
Open Access | Times Cited: 18
Enhancing Continuous Auditing with Large Language Models: A Framework for Cross-Verification Using Exogenous Textual Data
Huaxia Li, Marcelo Machado de Freitas, Heejae Lee, et al.
SSRN Electronic Journal (2024)
Closed Access | Times Cited: 7
Artificial intelligence co-piloted auditing
Hanchi Gu, Marco Schreyer, Kevin Moffitt, et al.
International Journal of Accounting Information Systems (2024) Vol. 54, pp. 100698-100698
Closed Access | Times Cited: 7
Understanding LLMs: A comprehensive overview from training to inference
Yiheng Liu, Hao He, Tianle Han, et al.
Neurocomputing (2024), pp. 129190-129190
Closed Access | Times Cited: 6
NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark
Oscar Sainz, Jon Ander Campos, Iker García-Ferrero, et al.
(2023)
Open Access | Times Cited: 13
Evaluation of Medium-large Language Models at Zero-shot Closed Book Generative Question Answering
René Peinl, Johannes Wirth
Artificial Intelligence and Applications (2024), pp. 93-111
Open Access | Times Cited: 5
R-CoT: Reinforcement Chain of Thought Prompting for Task Specific Training
Hsien‐Tang Chiu, I-K Fang, Che–Rung Lee
Communications in computer and information science (2025), pp. 157-167
Closed Access
Unicorn: A Unified Multi-tasking Model for Supporting Matching Tasks in Data Integration
Jianhong Tu, Ju Fan, Nan Tang, et al.
Proceedings of the ACM on Management of Data (2023) Vol. 1, Iss. 1, pp. 1-26
Closed Access | Times Cited: 12
Learning Customized Visual Models with Retrieval-Augmented Knowledge
Haotian Liu, Kilho Son, Jianwei Yang, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023), pp. 15148-15158
Open Access | Times Cited: 12
TARJAMAT: Evaluation of Bard and ChatGPT on Machine Translation of Ten Arabic Varieties
Karima Kadaoui, Samar Mohamed Magdy, Abdul Waheed, et al.
(2023), pp. 52-75
Open Access | Times Cited: 12
CoEdIT: Text Editing by Task-Specific Instruction Tuning
Vipul Raheja, Dhruv Kumar, Ryan Koo, et al.
(2023)
Open Access | Times Cited: 12
LLM–Assisted Data Augmentation for Chinese Dialogue–Level Dependency Parsing
Meishan Zhang, Gongyao Jiang, Shuang Liu, et al.
Computational Linguistics (2024) Vol. 50, Iss. 3, pp. 867-891
Open Access | Times Cited: 4
MOSS: An Open Conversational Large Language Model
Tianxiang Sun, Xiaotian Zhang, Zhengfu He, et al.
Deleted Journal (2024) Vol. 21, Iss. 5, pp. 888-905
Open Access | Times Cited: 4
Large Language Models for Intelligent Transportation: A Review of the State of the Art and Challenges
Sebastian Wandelt, Changhong Zheng, Shuang Wang, et al.
Applied Sciences (2024) Vol. 14, Iss. 17, pp. 7455-7455
Open Access | Times Cited: 4
Large language models: a survey of their development, capabilities, and applications
Yadagiri Annepaka, Partha Pakray
Knowledge and Information Systems (2024)
Closed Access | Times Cited: 4
Towards evaluating and building versatile large language models for medicine
Chaoyi Wu, Pengcheng Qiu, J Liu, et al.
npj Digital Medicine (2025) Vol. 8, Iss. 1
Open Access
CollectiveSFT: Scaling Large Language Models for Chinese Medical Benchmark with Collective Instructions in Healthcare
Jingwei Zhu, Minghuan Tan, Min Yang, et al.
Lecture notes in computer science (2025), pp. 51-60
Closed Access
Improving unified information extraction in Chinese mental health domain with instruction-tuned LLMs and type-verification component
Zheng Cai, Hui Fang, Jianhua Liu, et al.
Artificial Intelligence in Medicine (2025) Vol. 162, pp. 103087-103087
Closed Access
Through the Thicket: A Study of Number-Oriented LLMS Derived from Random Forest Models
Michał Romaszewski, Przemysław Sekuła, Przemysław Głomb, et al.
Journal of Artificial Intelligence and Soft Computing Research (2025) Vol. 15, Iss. 3, pp. 279-298
Open Access

Scroll to top