认知专家语言模型更好地与相应的大脑系统对齐

中文摘要 AI 辅助

大型语言模型（LLMs）在自然语言理解过程中能够预测人类大脑多个脑区的活动。然而，通常LLM-大脑对齐的测量是使用一个模型来对应大脑的不同区域，然后汇总各区域的模型性能。这种“一模型适配所有”的方法忽略了脑区的功能专门化。在本研究中，我们评估了面向特定认知领域的模型是否与该领域对应的大脑系统对齐得更好。通过提示和微调，我们首先为六个领域构建了专家LLM变体：感觉、空间、数字、推理、社会和抽象处理。然后，我们检验每个专家是否最能预测与相应认知领域相关的脑区活动。与我们的假设一致，每个专家的表征与匹配领域最相关的大脑系统的对齐程度高于其他专家。这在提示和微调两种方式下均成立，跨越三个基础模型和三个fMRI数据集。在一系列控制分析中，我们表明这种模型-大脑对齐对认知领域干预具有特异性；非认知和表面层面的干预不会产生类似的对齐效果。专门化模型会改变区域对齐，而总体预测准确性基本保持不变，这表明跨区域汇总对齐可能会掩盖特定模型在区域性能上的差异。

英文摘要

Large language models (LLMs) can predict human brain activity across a variety of brain regions during natural language comprehension. Typically, however, LLM-brain alignment is measured using one model for different regions of the brain, and then model performance is summarized across regions. This one-model-fits-all approach ignores the functional specialization of brain regions. In this study, we assess whether a model oriented toward a particular cognitive domain aligns better with the brain system dedicated to that domain. Through prompting and fine-tuning, we first build expert LLM variants for six domains: sensory, spatial, numerical, reasoning, social, and abstract processing. We then examine whether each expert best predicts activity in the brain region associated with the corresponding cognitive domain. Consistent with our hypotheses, each expert's representations align more closely with the brain system most associated with the matching domain than do other experts. This holds under both prompting and fine-tuning, across three base models and three fMRI datasets. In a series of control analyses, we show that this model-brain alignment is specific to cognitive domain interventions; non-cognitive and surface-level interventions do not result in comparable alignment. Specializing models shifts regional alignment while leaving aggregate prediction accuracy largely unchanged, suggesting that summarizing alignment across regions may obscure regional differences in performance for specific models.

发表机构

• University of Southern California（南加州大学）

机构由 AI 辅助整理，请以论文原文为准。