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        <title>Do Question Answering Modeling Improvements Hold Across Benchmarks?</title>
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    <abstract>Do question answering (QA) modeling improvements (e.g., choice of architecture and training procedure) hold consistently across the diverse landscape of QA benchmarks? To study this question, we introduce the notion of concurrence—two benchmarks have high concurrence on a set of modeling approaches if they rank the modeling approaches similarly. We measure the concurrence between 32 QA benchmarks on a set of 20 diverse modeling approaches and find that human-constructed benchmarks have high concurrence amongst themselves, even if their passage and question distributions are very different. Surprisingly, even downsampled human-constructed benchmarks (i.e., collecting less data) and programmatically-generated benchmarks (e.g., cloze-formatted examples) have high concurrence with human-constructed benchmarks. These results indicate that, despite years of intense community focus on a small number of benchmarks, the modeling improvements studied hold broadly.</abstract>
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%0 Conference Proceedings
%T Do Question Answering Modeling Improvements Hold Across Benchmarks?
%A Liu, Nelson F.
%A Lee, Tony
%A Jia, Robin
%A Liang, Percy
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F liu-etal-2023-question
%X Do question answering (QA) modeling improvements (e.g., choice of architecture and training procedure) hold consistently across the diverse landscape of QA benchmarks? To study this question, we introduce the notion of concurrence—two benchmarks have high concurrence on a set of modeling approaches if they rank the modeling approaches similarly. We measure the concurrence between 32 QA benchmarks on a set of 20 diverse modeling approaches and find that human-constructed benchmarks have high concurrence amongst themselves, even if their passage and question distributions are very different. Surprisingly, even downsampled human-constructed benchmarks (i.e., collecting less data) and programmatically-generated benchmarks (e.g., cloze-formatted examples) have high concurrence with human-constructed benchmarks. These results indicate that, despite years of intense community focus on a small number of benchmarks, the modeling improvements studied hold broadly.
%R 10.18653/v1/2023.acl-long.736
%U https://aclanthology.org/2023.acl-long.736
%U https://doi.org/10.18653/v1/2023.acl-long.736
%P 13186-13218

Markdown (Informal)

[Do Question Answering Modeling Improvements Hold Across Benchmarks?](https://aclanthology.org/2023.acl-long.736) (Liu et al., ACL 2023)

• Do Question Answering Modeling Improvements Hold Across Benchmarks? (Liu et al., ACL 2023)

ACL

• Nelson F. Liu, Tony Lee, Robin Jia, and Percy Liang. 2023. Do Question Answering Modeling Improvements Hold Across Benchmarks?. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13186–13218, Toronto, Canada. Association for Computational Linguistics.