当语言模型失去理智：大脑错位的后果
When Language Models Lose Their Mind: The Consequences of Brain Misalignment
AI总结：
研究探讨了大脑对齐对语言能力的影响，通过创建大脑错位模型发现其显著损害下游性能，强调了大脑对齐在实现稳健语言能力中的关键作用。
摘要：
尽管大脑对齐的大语言模型（LLMs）因其作为认知模型的潜力和在AI中的安全性和可信度的潜在应用而受到关注，但这种大脑对齐对语言能力的作用仍不确定。在本工作中，我们通过引入大脑错位模型——即故意训练以预测大脑活动表现差但保持高语言建模性能的LLMs，来研究大脑对齐的功能影响。我们评估这些模型在超过200个下游任务上的表现，涵盖多样化的语言领域，包括语义、语法、论述、推理和形态学。通过将大脑错位模型与良好匹配的大脑对齐模型进行比较，我们隔离了大脑对齐对语言理解的具体影响。我们的实验发现，大脑错位显著损害下游性能，突显了大脑对齐在实现稳健语言能力中的关键作用。这些发现强调了大脑对齐在LLMs中的重要性，并提供了关于神经表示与语言处理之间关系的新见解。
英文摘要：
While brain-aligned large language models (LLMs) have garnered attention for their potential as cognitive models and for potential for enhanced safety and trustworthiness in AI, the role of this brain alignment for linguistic competence remains uncertain. In this work, we investigate the functional implications of brain alignment by introducing brain-misaligned models--LLMs intentionally trained to predict brain activity poorly while maintaining high language modeling performance. We evaluate these models on over 200 downstream tasks encompassing diverse linguistic domains, including semantics, syntax, discourse, reasoning, and morphology. By comparing brain-misaligned models with well-matched brain-aligned counterparts, we isolate the specific impact of brain alignment on language understanding. Our experiments reveal that brain misalignment substantially impairs downstream performance, highlighting the critical role of brain alignment in achieving robust linguistic competence. These findings underscore the importance of brain alignment in LLMs and offer novel insights into the relationship between neural representations and linguistic processing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
