Rissanen数据分析：通过描述长度检查数据集特征
Rissanen Data Analysis: Examining Dataset Characteristics via Description Length
摘要：
我们介绍了一种方法来确定是否某种能力有助于实现给定数据的准确模型。我们认为标签是由具有不同功能的子程序组成的程序从输入中生成的，并且我们认为子程序是有用的，当且仅当调用它的最小程序比不调用它的程序短。由于最小程序长度无法计算，因此我们转而估计标签的最小描述长度（MDL）作为代理，为我们提供了一种分析数据集特征的理论依据方法。我们将该方法称为MDL之父Rissanen Data Analysis（RDA），并展示了其在NLP中各种设置上的适用性，从评估在回答问题之前生成子问题的效用，到分析理由和解释的价值，再到调查不同词性的重要性，以及揭示数据集的性别偏见。
英文摘要：
We introduce a method to determine if a certain capability helps to achieve an accurate model of given data. We view labels as being generated from the inputs by a program composed of subroutines with different capabilities, and we posit that a subroutine is useful if and only if the minimal program that invokes it is shorter than the one that does not. Since minimum program length is uncomputable, we instead estimate the labels' minimum description length (MDL) as a proxy, giving us a theoretically-grounded method for analyzing dataset characteristics. We call the method Rissanen Data Analysis (RDA) after the father of MDL, and we showcase its applicability on a wide variety of settings in NLP, ranging from evaluating the utility of generating subquestions before answering a question, to analyzing the value of rationales and explanations, to investigating the importance of different parts of speech, and uncovering dataset gender bias.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
