DQI: A Guide to Benchmark Evaluation

• URL: http://arxiv.org/abs/2008.03964v1
• Date: Mon, 10 Aug 2020 08:38:55 GMT
• Title: DQI: A Guide to Benchmark Evaluation
• Authors: Swaroop Mishra, Anjana Arunkumar, Bhavdeep Sachdeva, Chris Bryan and Chitta Baral
• Abstract summary: A model A surpasses humans in a benchmark B, but fails on similar benchmarks C, D, and E. We propose a novel approach to solve this underexplored task of quantifying benchmark quality by debuting a data quality metric: DQI.
• Score: 22.54066527822898
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A `state of the art' model A surpasses humans in a benchmark B, but fails on similar benchmarks C, D, and E. What does B have that the other benchmarks do not? Recent research provides the answer: spurious bias. However, developing A to solve benchmarks B through E does not guarantee that it will solve future benchmarks. To progress towards a model that `truly learns' an underlying task, we need to quantify the differences between successive benchmarks, as opposed to existing binary and black-box approaches. We propose a novel approach to solve this underexplored task of quantifying benchmark quality by debuting a data quality metric: DQI.

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