🚀Excited to share our latest work: LLMs entangle language and knowledge, making it hard to verify or update facts. We introduce LMLM 🐑🧠 — a new class of models that externalize factual knowledge into a database and learn during pretraining when and how to retrieve facts instead of memorizing them. 🧠Why LMLM? • Learning to look up facts is easier than memorization • Externalizing knowledge improves factual precision • Enables instant machine unlearning by design LMLM opens new directions for how future language models can manage and access knowledge. 📄 [ArXiv] arxiv.org/pdf/2505.15962 🌐 [Project Page] linxi-zhao.github.io/LMLM-si… 💻 [Code] github.com/kilian-group/LMLM 🎤 [Talk] simons.berkeley.edu/talks/ki… Huge thanks to my amazing collaborators: @linxizhao4 @sofianzalouk Christian Belardi Justin Lovelace @JinPZhou And to our incredible advisors @KilianQW, @yoavartzi, and @JenJSun for their generous support and insight.