脑评分追踪语言的共享属性：来自多种自然语言和结构序列的证据
Brain Score Tracks Shared Properties of Languages: Evidence from Many Natural Languages and Structured Sequences
AI总结：
研究通过训练语言模型并评估其脑评分表现，发现多种自然语言和结构化数据在脑评分上表现相似，表明模型能提取语言共性，但高评分不一定代表人类级处理能力。
摘要：
近期基于神经网络的语言模型突破引发了关于这些模型处理方式与人类语言处理相似性的疑问。通过使用称为脑评分（BS）的框架，即从语言模型激活预测fMRI激活，研究发现训练在多种自然语言上的模型在BS表现上非常相似。此外，训练在其他结构化数据（如人类基因组、Python和纯层次结构）上的语言模型也表现出色，某些情况下接近自然语言。这些发现表明，BS可以突出语言模型提取自然语言共同结构的能力，但该指标可能不够敏感，无法仅凭高BS分数推断出人类级处理能力。
英文摘要：
Recent breakthroughs in language models (LMs) using neural networks have raised the question: how similar are these models' processing to human language processing? Results using a framework called Brain Score (BS) -- predicting fMRI activations during reading from LM activations -- have been used to argue for a high degree of similarity. To understand this similarity, we conduct experiments by training LMs on various types of input data and evaluate them on BS. We find that models trained on various natural languages from many different language families have very similar BS performance. LMs trained on other structured data -- the human genome, Python, and pure hierarchical structure (nested parentheses) -- also perform reasonably well and close to natural languages in some cases. These findings suggest that BS can highlight language models' ability to extract common structure across natural languages, but that the metric may not be sensitive enough to allow us to infer human-like processing from a high BS score alone.
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