Title: Fine-grained Analysis of Brain-LLM Alignment through Input Attribution
Primary area: applications to neuroscience & cognitive science
Keywords: Brain alignment, Brain-LLM aligment, Input attribution, Next word prediction, Explainiable artificial intelligence

Abstract: Understanding the alignment between large language models (LLMs) and human brain activity can reveal computational principles underlying language processing. We introduce a fine-grained input attribution method to identify the specific words most important for brain-LLM alignment, and leverage it to study a contentious research question about brain-LLM alignment: the relationship between brain alignment (BA) and next-word prediction (NWP). Our findings reveal that BA and NWP rely on largely distinct word subsets: NWP exhibits recency and primacy biases with a focus on syntax, while BA prioritizes semantic and discourse-level information with a more targeted recency effect. This work advances our understanding of how LLMs relate to human language processing and highlights differences in feature reliance between BA and NWP. Beyond this study, our attribution method can be broadly applied to explore the cognitive relevance of model predictions in diverse language processing tasks.

TL;DR: Input attribution reveals why LLMs align with brain activity, highlighting both shared semantics and distinct mechanisms compared with next‑word prediction.