When Language Models Lose Their Mind: The Consequences of Brain Misalignment
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-ma...
| Authors | Gabriele Merlin, Mariya Toneva |
|---|---|
| Primary category | cs.CL |
| Published | 24 March 2026 |
| Last updated at source | 24 March 2026 |
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