This paper takes a first step towards a critical thinking curriculum for neural auto-regressive language models. We introduce a synthetic corpus of deductively valid arguments, and generate artificial argumentative texts to train and evaluate GPT-2. Significant transfer learning effects can be observed: Training a model on three simple core schemes allows it to accurately complete conclusions of different, and more complex types of arguments, too. The language models generalize the core argument schemes in a correct way. Moreover, we obtain consistent and promising results for NLU benchmarks. In particular, pre-training on the argument schemes raises zero-shot accuracy on the GLUE diagnostics by up to 15 percentage points. The findings suggest that intermediary pre-training on texts that exemplify basic reasoning abilities (such as typically covered in critical thinking textbooks) might help language models to acquire a broad range of reasoning skills. The synthetic argumentative texts presented in this paper are a promising starting point for building such a "critical thinking curriculum for language models."
翻译:本文为神经自动递减语言模型的批判性思维课程迈出了第一步。 我们引入了一套综合的推论有效参数,并生成了用于培训和评估GPT-2的人工论证文本。 可以看到重要的转移学习效果: 培训了三种简单核心方案的模式,它也能够准确地完成不同和更为复杂的争论类型的结论。 语言模型以正确的方式概括了核心论证方案。 此外,我们为NLU基准取得了一致和有希望的结果。 特别是,关于论证方案的培训前提高了GLUE诊断的零射速精确度,提高了15个百分点。 研究结果表明,对体现基本推理能力(例如典型的关键思维教科书所涵盖的)的文本进行中期培训,可能有助于语言模型获得广泛的推理技巧。 本文提出的综合论证文本是建立这种“语言模型批判性思维课程”的一个有希望的起点。