Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. In our eighth grade classroom, her name was Hanna. On AOL Instant Messenger, she was Banana3017. I was in love with both. At school, she was funny, and kind, and she had blue eyes that made my cheeks glow the same fiery color as her hair when she looked at me.

Large Language Models (LLMs) often retain inaccurate or outdated information from pre-training, leading to incorrect predictions or biased outputs during inference. While existing model editing methods can address this challenge, they struggle with editing large amounts of factual information simultaneously and may compromise the general capabilities of the models. In this paper, our empirical study demonstrates that it is feasible to edit the internal representations of LLMs and replace the entities in a manner similar to editing natural language inputs. Based on this insight, we introduce the Latent Knowledge Scalpel (LKS), an LLM editor that manipulates the latent knowledge of specific entities via a lightweight hypernetwork to enable precise and large-scale editing. Experiments conducted on Llama-2 and Mistral show even with the number of simultaneous edits reaching 10,000, LKS effectively performs knowledge editing while preserving the general abilities of the edited LLMs. Code is available at: https://github.com/Linuxin-xxx/LKS.

In each case it is a near-certainty that their quick thinking, powered by the uniquely human ability to combine high levels of training and proficiency with creativity at critical moments, saved lives. Specifically a total of 875 lives were saved by these airmen and their crew mates in these six headline-grabbing incidents. It also is a near-certainty that no technology available today, and none that is likely to be available for use in the next generation or two of commercial aircraft (which will be in service at least through the middle of this century) is or will be capable of the kind of rapid-fire, mentally elastic and way outside-the-box thinking that each of these pilots demonstrated. Significant technical, regulatory, spectrum/bandwidth, artificial intelligence, financial and insurance barriers are but the most obvious challenges.