A Survey on Out-of-Distribution Evaluation of Neural NLP Models
Abstract
Adversarial robustness, domain generalization and dataset biases are three active lines of research contributing to out-of-distribution (OOD) evaluation on neural NLP models. However, a comprehensive, integrated discussion of the three research lines is still lacking in the literature. This survey will 1) compare the three lines of research under a unifying definition; 2) summarize their data-generating processes and evaluation protocols for each line of research; and 3) emphasize the challenges and opportunities for future work.
Cite
Text
Li et al. "A Survey on Out-of-Distribution Evaluation of Neural NLP Models." International Joint Conference on Artificial Intelligence, 2023. doi:10.24963/IJCAI.2023/749Markdown
[Li et al. "A Survey on Out-of-Distribution Evaluation of Neural NLP Models." International Joint Conference on Artificial Intelligence, 2023.](https://mlanthology.org/ijcai/2023/li2023ijcai-survey/) doi:10.24963/IJCAI.2023/749BibTeX
@inproceedings{li2023ijcai-survey,
title = {{A Survey on Out-of-Distribution Evaluation of Neural NLP Models}},
author = {Li, Xinzhe and Liu, Ming and Gao, Shang and Buntine, Wray L.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2023},
pages = {6683-6691},
doi = {10.24963/IJCAI.2023/749},
url = {https://mlanthology.org/ijcai/2023/li2023ijcai-survey/}
}