Recent advancements in artificial intelligence have sparked interest in the
parallels between large language models (LLMs) and human neural processing,
particularly in language comprehension. While prior research has established
similarities in the representation of LLMs and the brain, the underlying
computational principles that cause this convergence, especially in the context
of evolving LLMs, remain elusive. Here, we examined a diverse selection of
high-performance LLMs with similar parameter sizes to investigate the factors
contributing to their alignment with the brain's language processing
mechanisms. We find that as LLMs achieve higher performance on benchmark tasks,
they not only become more brain-like as measured by higher performance when
predicting neural responses from LLM embeddings, but also their hierarchical
feature extraction pathways map more closely onto the brain's while using fewer
layers to do the same encoding. We also compare the feature extraction pathways
of the LLMs to each other and identify new ways in which high-performing models
have converged toward similar hierarchical processing mechanisms. Finally, we
show the importance of contextual information in improving model performance
and brain similarity. Our findings reveal the converging aspects of language
processing in the brain and LLMs and offer new directions for developing models
that align more closely with human cognitive processing.

通过研究使用大型语言模型（LLMs）探索人工神经处理和语言理解之间相似性的因素，我们发现随着 LLMs 在基准任务上性能的提高，它们不仅在预测 LLM 嵌入的神经相应性时表现出更高的脑部相似性，而且它们的分层特征提取路径与大脑的映射更为密切，并且使用较少的层进行相同的编码。此外，我们还比较了不同 LLMs 的特征提取路径，发现高性能模型在层次化处理机制方面更多地趋同。最后，我们展示了上下文信息在提高模型性能和与大脑相似性方面的重要性，并揭示了大脑和 LLMs 语言处理的融合特点，为开发更加与人类认知处理密切相关的模型提供了新方向。