The field of language models is moving towards improving their ability to reason and edit knowledge in a logically consistent manner. Recent research has focused on addressing the reversal curse, a fundamental limitation in language models that prevents them from inferring unseen facts. Innovations in training methods and model architectures have led to the emergence of bilinear relational structures, which enable language models to behave in a more logically consistent way after editing. Additionally, there is a growing interest in developing more robust and efficient editing frameworks that can mitigate the unintended side effects of retraining and support precise model updates. Noteworthy papers in this area include:

• A study on bilinear relational structure that enables consistent model editing and alleviates the reversal curse.
• A paper on REPAIR, a lifelong editing framework that supports precise and low-cost model updates while preserving non-target knowledge.