Reversal Curse

The "reversal curse" describes the failure of large language models (LLMs) and other machine learning systems to generalize knowledge bidirectionally; for example, learning "A is B" but failing to infer "B is A." Current research focuses on mitigating this limitation through techniques like reverse training, bidirectional attention mechanisms, and semantic-aware data permutation, often applied within transformer-based architectures. Overcoming the reversal curse is crucial for improving the reasoning capabilities of AI systems and ensuring their reliable application in knowledge representation, question answering, and other knowledge-intensive tasks.