Existing image-text modality alignment in Vision Language Models (VLMs) treats each text token equally in an autoregressive manner.

Model editing has become an increasingly popular method for efficiently updating knowledge within language models. Current approaches primarily focus on reliability, generalization, and locality, with many excelling across these criteria.

Because LMs now surpass the performance of most humans except domain experts, evaluating them becomes increasingly challenging.