Pretrained language models that have been trained to predict the next word over billions of text documents have been shown to also significantly predict brain recordings of people comprehending language. Understanding the reasons behind the observed similarities between language in machines and language in the brain can lead to more insight into both systems. Recent works suggest that the prediction of the next word is a key mechanism that contributes to the alignment between the two. What is not yet understood is whether prediction of the next word is necessary for this observed alignment or simply sufficient, and whether there are other shared mechanisms or information that is similarly important. In this work, we take a first step towards a better understanding via two simple perturbations in a popular pretrained language model. The first perturbation is to improve the model's ability to predict the next word in the specific naturalistic stimulus text that the brain recordings correspond to. We show that this indeed improves the alignment with the brain recordings. However, this improved alignment may also be due to any improved word-level or multi-word level semantics for the specific world that is described by the stimulus narrative. We aim to disentangle the contribution of next word prediction and semantic knowledge via our second perturbation: scrambling the word order at inference time, which reduces the ability to predict the next word, but maintains any newly learned word-level semantics. By comparing the alignment with brain recordings of these differently perturbed models, we show that improvements in alignment with brain recordings are due to more than improvements in next word prediction and word-level semantics.
翻译:受过训练可以预测数十亿多文本文件下一个字数的预知性语言模型已经显示,受过培训可以预测数十亿多文本文件的下一个字字的预示性语言模型,也大大预测了人们理解语言语言语言的大脑记录。 了解机器语言和大脑语言之间观察到的相似性背后的原因,可以导致对两种系统有更深入的了解。 最近的工作表明,对下一个字的预测是一个关键机制,有助于两者之间的一致。 尚不能理解的是,对下一个字词的预测是否必要,这种预测是否足够,是否还有其他共享机制或信息也同样重要。 在这项工作中,我们迈出了第一步,通过流行的先行语言培训语言模型的两个简单的扰动,来更好地了解人们的大脑记录。 第一次扰动是为了提高模型在大脑录音中预测下一个字词的能力。 我们表明,这确实改善了与大脑记录的一致性。 然而,这种改进也可能是由于对特定世界的字级或多字级调整性比刺激说明所描述的改进程度要好。 我们的目标是通过下一个字级预测和语性知识水平来混淆下一个字数的预测和精度水平,通过我们每个字根级记录来降低字型的字型的字形变。