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arXiv · 2301.09406

The Reasonable Effectiveness of Diverse Evaluation Data

Lora Aroyo·Mark Diaz·Christopher Homan·Vinodkumar Prabhakaran·Alex Taylor·Ding Wang
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Abstract

In this paper, we present findings from an semi-experimental exploration of rater diversity and its influence on safety annotations of conversations generated by humans talking to a generative AI-chat bot. We find significant differences in judgments produced by raters from different geographic regions and annotation platforms, and correlate these perspectives with demographic sub-groups. Our work helps define best practices in model development -- specifically human evaluation of generative models -- on the backdrop of growing work on sociotechnical AI evaluations.

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Lora Aroyo, Mark Diaz, Christopher Homan, Vinodkumar Prabhakaran, Alex Taylor, Ding Wang. 2023-01-23. The Reasonable Effectiveness of Diverse Evaluation Data. https://arxiv.org/abs/2301.09406

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