<ul><li>Despite their impressive capabilities, LLMs exhibit a basic generalization failure known as the Reversal Curse.</li><li>The Reversal Curse in LLMs is attributed to the long-standing binding problem in cognitive science, neuroscience, and AI.</li><li>Transformers' limitations in conceptual binding cause the inconsistency and entanglements of concept representations, leading to the Reversal Curse.</li><li>A model design based on JEPA (Joint-Embedding Predictive Architecture) breaks the Reversal Curse and improves generalization by incorporating memory layers supporting disentangled concept representations.</li></ul>

Is the Reversal Curse a Binding Problem? Uncovering Limitations of Transformers from a Basic Generalization Failure

Discover more