Representations from transformer-based unidirectional language models are known to be effective at predicting brain responses to natural language. However, most studies comparing language models to brains have used GPT-2 or similarly sized language models. Here we tested whether larger open-source models such as those from the OPT and LLaMA families are better at predicting brain responses recorded using fMRI. Mirroring scaling results from other contexts, we found that brain prediction performance scales logarithmically with model size from 125M to 30B parameter models, with ~15% increased encoding performance as measured by correlation with a held-out test set across 3 subjects. Similar logarithmic behavior was observed when scaling the size of the fMRI training set. We also characterized scaling for acoustic encoding models that use HuBERT, WavLM, and Whisper, and we found comparable improvements with model size. A noise ceiling analysis of these large, high-performance encoding models showed that performance is nearing the theoretical maximum for brain areas such as the precuneus and higher auditory cortex. These results suggest that increasing scale in both models and data will yield incredibly effective models of language processing in the brain, enabling better scientific understanding as well as applications such as decoding.

翻译：基于Transformer的单向语言模型的表征已被证明能有效预测大脑对自然语言的反应。然而，大多数将语言模型与大脑进行比较的研究都使用了GPT-2或类似规模的语言模型。在此，我们测试了更大的开源模型（如OPT和LLaMA系列）是否能更好地预测fMRI记录的大脑反应。与其它领域的缩放结果一致，我们发现大脑预测性能随模型规模从1.25亿到300亿参数呈对数增长，在3名受试者中，与保留测试集的相关性衡量表明编码性能提升约15%。在缩放fMRI训练集大小时也观察到类似的对数行为。我们还刻画了使用HuBERT、WavLM和Whisper的声学编码模型的缩放特性，并发现模型规模带来的改进同样显著。对这些大型高性能编码模型的噪声上限分析表明，在前楔叶和高级听觉皮层等脑区，其性能已接近理论最大值。这些结果表明，增加模型和数据的规模将产生极其有效的脑语言处理模型，从而促进更深入的科学理解以及解码等应用。