The Reversal Curse
Finds that LLMs trained on "A is B" fail to generalize to "B is A" - a surprisingly deep failure of learning.
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Asymmetric fact learning: LLMs finetuned on statements of the form "A is B" show no ability to answer "Who is B?" with A, even after extensive training.
Fictitious-statement testbed: Demonstrates the effect using fine-tuning on fictitious statements, so training data can't contribute the reverse direction through coincidence.
Model-family robust: The Reversal Curse persists across different model sizes and model families, suggesting it reflects a fundamental property of next-token prediction training.
Knowledge representation implication: Raises hard questions about how LLMs represent knowledge - they clearly don't store bidirectional relations by default, unlike symbolic knowledge bases.