Bilinear representation mitigates reversal curse and enables consistent model editing

Researchers have identified that the 'reversal curse' in language models - their inability to infer 'B is A' from 'A is B' - can be overcome through bilinear representation structures. Training models on synthetic relational knowledge graphs creates internal geometries that enable consistent model editing and logical inference of reverse facts.

• →The reversal curse is not an inherent limitation but an artifact of how models encode knowledge.
• →Training on relational knowledge graphs leads to bilinear representation structures that solve the reversal curse.
• →Bilinear geometry enables consistent model editing where updates propagate correctly to related facts.
• →Models without bilinear representations suffer from logical inconsistencies during editing.
• →The efficacy of language model editing depends on the underlying representational geometry, not just algorithms.