@inproceedings{awadalla-etal-2022-exploring,
title = "Exploring The Landscape of Distributional Robustness for Question Answering Models",
author = "Awadalla, Anas and
Wortsman, Mitchell and
Ilharco, Gabriel and
Min, Sewon and
Magnusson, Ian and
Hajishirzi, Hannaneh and
Schmidt, Ludwig",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.441/",
doi = "10.18653/v1/2022.findings-emnlp.441",
pages = "5971--5987",
abstract = "We conduct a large empirical evaluation to investigate the landscape of distributional robustness in question answering. Our investigation spans over 350 models and 16 question answering datasets, including a diverse set of architectures, model sizes, and adaptation methods (e.g., fine-tuning, adapter tuning, in-context learning, etc.). We find that, in many cases, model variations do not affect robustness and in-distribution performance alone determines out-of-distribution performance.Moreover, our findings indicate thati) zero-shot and in-context learning methods are more robust to distribution shifts than fully fine-tuned models;ii) few-shot prompt fine-tuned models exhibit better robustness than few-shot fine-tuned span prediction models;iii) parameter-efficient and robustness enhancing training methods provide no significant robustness improvements.In addition, we publicly release all evaluations to encourage researchers to further analyze robustness trends for question answering models."
}
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%0 Conference Proceedings
%T Exploring The Landscape of Distributional Robustness for Question Answering Models
%A Awadalla, Anas
%A Wortsman, Mitchell
%A Ilharco, Gabriel
%A Min, Sewon
%A Magnusson, Ian
%A Hajishirzi, Hannaneh
%A Schmidt, Ludwig
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F awadalla-etal-2022-exploring
%X We conduct a large empirical evaluation to investigate the landscape of distributional robustness in question answering. Our investigation spans over 350 models and 16 question answering datasets, including a diverse set of architectures, model sizes, and adaptation methods (e.g., fine-tuning, adapter tuning, in-context learning, etc.). We find that, in many cases, model variations do not affect robustness and in-distribution performance alone determines out-of-distribution performance.Moreover, our findings indicate thati) zero-shot and in-context learning methods are more robust to distribution shifts than fully fine-tuned models;ii) few-shot prompt fine-tuned models exhibit better robustness than few-shot fine-tuned span prediction models;iii) parameter-efficient and robustness enhancing training methods provide no significant robustness improvements.In addition, we publicly release all evaluations to encourage researchers to further analyze robustness trends for question answering models.
%R 10.18653/v1/2022.findings-emnlp.441
%U https://aclanthology.org/2022.findings-emnlp.441/
%U https://doi.org/10.18653/v1/2022.findings-emnlp.441
%P 5971-5987
Markdown (Informal)
[Exploring The Landscape of Distributional Robustness for Question Answering Models](https://aclanthology.org/2022.findings-emnlp.441/) (Awadalla et al., Findings 2022)
ACL