Evaluating the Reversal Curse in Model Editing

Understand

Large language models (LLMs) are prone to hallucinate unintended text due to false or outdated knowledge.

• Since retraining LLMs is resource intensive, there has been a growing interest in model editing.
• Despite the emergence of benchmarks and approaches, existing unidirectional editing and evaluation paradigms have failed to explore the reversal curse.
• In this paper, we study bidirectional language model editing, aiming to provide a rigorous evaluation to assess if edited LLMs can recall the editing knowledge bidirectionally.