DROP

Discrete Reasoning Over the content of Paragraphs

A dataset introduced to facilitate and measure the performance of natural language processing models on a complex question-answering task. The key characteristic of DROP is that it requires the model to perform discrete operations like addition, counting, or sorting to answer questions. These questions are based on a given passage of text.

In more detail, DROP presents questions that demand an understanding of the text and the ability to perform discrete reasoning—things like numerical reasoning, sorting events in chronological order, or determining the duration of an event given its start and end times. This goes beyond simple fact retrieval or paraphrasing the text’s content.

For instance, a typical question in DROP might involve a paragraph describing a sports game and asking, How many points were scored in total? To answer this correctly, an AI model must identify and add up the individual scoring actions mentioned in the text.

DROP is significant in the AI and machine learning (ML) field as it pushes the boundaries of what natural language processing models can do, moving them toward more complex and nuanced understanding and reasoning. In a sales and marketing context, the complex reasoning tested by DROP can be invaluable in analyzing customer feedback, understanding detailed customer inquiries, or processing complex data sets to derive actionable insights.

• Abbreviation: DROP