Recent advances in artificial intelligence have given rise to large language models (LLMs) that not only achieve human-like performance but also share computational principles with the brain's language processing mechanisms. While previous research has primarily focused on aligning LLMs' internal representations with neural activity, we introduce a novel approach that leverages explainable AI (XAI) methods to forge deeper connections between the two domains. Using attribution methods, we quantified how preceding words contribute to an LLM's next-word predictions and employed these explanations to predict fMRI recordings from participants listening to the same narratives. Our findings demonstrate that attribution methods robustly predict brain activity across the language network, surpassing traditional internal representations in early language areas. This alignment is hierarchical: early-layer explanations correspond to the initial stages of language processing in the brain, while later layers align with more advanced stages. Moreover, the layers more influential on LLM next-word prediction$\unicode{x2014}$those with higher attribution scores$\unicode{x2014}$exhibited stronger alignment with neural activity. This work establishes a bidirectional bridge between AI and neuroscience. First, we demonstrate that attribution methods offer a powerful lens for investigating the neural mechanisms of language comprehension, revealing how meaning emerges from preceding context. Second, we propose using brain alignment as a metric to evaluate the validity of attribution methods, providing a framework for assessing their biological plausibility.

该研究解决了传统方法无法有效对齐深度语言模型内部表征与大脑神经活动之间差距的问题。通过结合可解释人工智能方法，研究显示，前面单词对模型进行下一词预测的贡献可以有效预测参与者听同一叙述时的fMRI记录，揭示了大脑语言处理的层次结构及其与语言模型之间的关联。这为理解语言理解的神经机制提供了有力工具，并引入了用大脑对齐作为评估可解释性方法有效性的标准。

深度语言模型的解释与大脑中的语言表征解释