论文标题
一种因果框架，用于量化语言模型的数学推理的鲁棒性
A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models
论文作者
论文摘要
最近，我们在语言模型的艰难数学推理问题上见证了许多令人印象深刻的结果。同时，这些模型的鲁棒性也受到质疑。最近的作品表明，在生成解决方案时，模型可以依靠问题描述中的浅模式。在行为测试的概念的基础上，我们提出了一个新型框架，该框架降低了输入中各种因素的因果效应，例如问题文本的表面形式，操作数和数学运算符在输出解决方案上。通过在描述直观推理过程的因果图中对行为分析进行扎根，我们以鲁棒性和对输入空间中直接干预的敏感性来研究语言模型的行为。我们将框架应用于数学单词问题的测试床。我们的分析表明，与所有其他GPT变体相比，鲁棒性似乎并未随着尺寸的函数而不断提高，而GPT-3 Davinci模型（175b）在鲁棒和灵敏度方面取得了巨大的提高。
We have recently witnessed a number of impressive results on hard mathematical reasoning problems with language models. At the same time, the robustness of these models has also been called into question; recent works have shown that models can rely on shallow patterns in the problem description when generating a solution. Building on the idea of behavioral testing, we propose a novel framework, which pins down the causal effect of various factors in the input, e.g., the surface form of the problem text, the operands, and math operators on the output solution. By grounding the behavioral analysis in a causal graph describing an intuitive reasoning process, we study the behavior of language models in terms of robustness and sensitivity to direct interventions in the input space. We apply our framework on a test bed of math word problems. Our analysis shows that robustness does not appear to continuously improve as a function of size, but the GPT-3 Davinci models (175B) achieve a dramatic improvement in both robustness and sensitivity compared to all other GPT variants.
