Fetching the paper…
Reading the bibliography…
The dependence of Natural Language Processing (NLP) intelligent software on Large Language Models (LLMs) is increasingly prominent, underscoring the necessity for robustness testing.
P. Malo, A. Sinha, P. Korhonen, J. Wallenius, and P. Takala, “Good debt or bad debt: Detecting semantic orientations in economic texts,” Journal of the Association for Information Science and Technology
2014
Earlier work this paper cites.
X. Zhang, J. Zhao, and Y. LeCun, “Character-level convolutional networks for text classification,” Advances in neural information processing systems
2015
Earlier work this paper cites.
A. Naik, A. Ravichander, N. Sadeh, C. Rose, and G. Neubig, “Stress test evaluation for natural language inference,” in Proceedings of the 27th International Conference on Computational Linguistics
2018
Earlier work this paper cites.
J. Li, S. Ji, T. Du, B. Li, and T. Wang, “Textbugger: Generating adversarial text against real-world applications,” in Proceedings 2019 Network and Distributed System Security Symposium
2019
Earlier work this paper cites.
S. Ren, Y. Deng, K. He, and W. Che, “Generating natural language adversarial examples through probability weighted word saliency,” in Proceedings of the 57th annual meeting of the association for computational linguistics
2019
Earlier work this paper cites.
J. Morris, E. Lifland, J. Y. Yoo, J. Grigsby, D. Jin, and Y. Qi, “Textattack: A framework for adversarial attacks, data augmentation, and adversarial training in nlp,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
2020
Earlier work this paper cites.
D. Jin, Z. Jin, J. T. Zhou, and P. Szolovits, “Is bert really robust? a strong baseline for natural language attack on text classification and entailment,” in Proceedings of the AAAI conference on artificial intelligence
2020
Earlier work this paper cites.
M. T. Ribeiro, T. Wu, C. Guestrin, and S. Singh, “Beyond accuracy: Behavioral testing of nlp models with checklist,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
2020
Earlier work this paper cites.
P. Zhang, B. Ren, H. Dong, and Q. Dai, “Cagfuzz: coverage-guided adversarial generative fuzzing testing for image-based deep learning systems,” IEEE Transactions on Software Engineering
2021
Earlier work this paper cites.
Y. Xiao, Y. Lin, I. Beschastnikh, C. Sun, D. Rosenblum, and J. S. Dong, “Repairing failure-inducing inputs with input reflection,” in Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering
2022
Earlier work this paper cites.
K. Zhu, J. Wang, J. Zhou, Z. Wang, H. Chen, Y. Wang, L. Yang, W. Ye, N. Z. Gong, Y. Zhang, et al
2023
Cited alongside, same era.
2023
Cited alongside, same era.
M. Davis, S. Choi, S. Estep, B. Myers, and J. Sunshine, “Nanofuzz: A usable tool for automatic test generation,” in Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
2023
Cited alongside, same era.
2023
Cited alongside, same era.
J. Wang, X. Hu, W. Hou, H. Chen, R. Zheng, Y. Wang, L. Yang, H. Huang, W. Ye, X. Geng, et al
2023
Later among the works it cites.
C.-Y. Ko, P.-Y. Chen, P. Das, Y.-S. Chuang, and L. Daniel, “On robustness-accuracy characterization of large language models using synthetic datasets,” in International Conference on Machine Learning
2023
Later among the works it cites.
M. Xiao, Y. Xiao, H. Dong, S. Ji, and P. Zhang, “Leap: Efficient and automated test method for nlp software,” in 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)
2023
Later among the works it cites.
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
C. B. Head, P. Jasper, M. McConnachie, L. Raftree, and G. Higdon, “Large language model applications for evaluation: Opportunities and ethical implications,” New Directions for Evaluation
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
X. He, Z. Lin, Y. Gong, A. Jin, H. Zhang, C. Lin, J. Jiao, S. M. Yiu, N. Duan, W. Chen, et al
2023
Later among the works it cites.
L. Espinosa and M. Salathé, “Use of large language models as a scalable approach to understanding public health discourse,” medRxiv
2024
Closest in time.