论文标题
通过四个实验探针了解基于变压器的语言表示模型中先前的偏见和选择瘫痪
Understanding Prior Bias and Choice Paralysis in Transformer-based Language Representation Models through Four Experimental Probes
论文作者
论文摘要
关于基于变形金刚的神经网络的最新工作导致了多项选择自然语言理解（NLU）问题的令人印象深刻的进步，例如问答（QA）和绑架推理。尽管有这些进步，但仍有有限的工作在理解这些模型是否以足够强大的方式对扰动的多项选择实例做出反应，这将使他们在现实世界中得到信任。我们提出了四个混乱探针，灵感来自于行为科学界最初发现的类似现象，以测试诸如先前的偏见和选择瘫痪等问题。在实验上，我们使用四个已建立的基准数据集探测了一种广泛使用的基于变压器的多项选择NLU系统。在这里，我们表明该模型还表现出明显的先前偏见，并且除了其他问题之外，还具有较小但高度的选择性瘫痪程度。我们的结果表明，在将语言模型用于面向面向系统或具有现实世界后果的决策之前，可能需要使用更强大的测试协议和其他基准。
Recent work on transformer-based neural networks has led to impressive advances on multiple-choice natural language understanding (NLU) problems, such as Question Answering (QA) and abductive reasoning. Despite these advances, there is limited work still on understanding whether these models respond to perturbed multiple-choice instances in a sufficiently robust manner that would allow them to be trusted in real-world situations. We present four confusion probes, inspired by similar phenomena first identified in the behavioral science community, to test for problems such as prior bias and choice paralysis. Experimentally, we probe a widely used transformer-based multiple-choice NLU system using four established benchmark datasets. Here we show that the model exhibits significant prior bias and to a lesser, but still highly significant degree, choice paralysis, in addition to other problems. Our results suggest that stronger testing protocols and additional benchmarks may be necessary before the language models are used in front-facing systems or decision making with real world consequences.
