Do Question Answering Modeling Improvements Hold Across Benchmarks?

• URL: http://arxiv.org/abs/2102.01065v3
• Date: Tue, 30 May 2023 20:50:47 GMT
• Title: Do Question Answering Modeling Improvements Hold Across Benchmarks?
• Authors: Nelson F. Liu and Tony Lee and Robin Jia and Percy Liang
• Abstract summary: We measure concurrence between 32 QA benchmarks on a set of 20 diverse modeling approaches. Despite years of intense community focus on a small number of benchmarks, the modeling improvements studied hold broadly.
• Score: 84.48867898593052
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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