This is a brand new framework for LLM knowledge editing (KE) that solves the critical problems of ambiguity in previous KE methods. A very exciting work!

🚀Excited to share our new work at NAACL 2024! arxiv.org/pdf/2404.01652… We study a critical generalization issue of retrieval-augmented generation (RAG) systems - how to maintain the model performance as global knowledge shifts and the background corpus evolves. 🌎✨ 🔎Our key

👾 Introducing AgentOccam: Automating Web Tasks with LLMs! 🌐 AgentOccam showcases the impressive power of Large Language Models (LLMs) on web tasks, without any in-context examples, new agent roles, online feedback, or search strategies. 🏄🏄🏄 🧙 Link: arxiv.org/abs/2410.13825

I am at #EMNLP2024! I will present our work "Why Does New Knowledge Create Messy Ripple Effects in LLMs? " on Web 10:30am. Thanks to all the collaborators Heng Ji Zixuan Zhang Chi Han Manling Li Looking forward to have a chat! Paper Link: arxiv.org/pdf/2407.12828

🙌 Happy New Year everyone! 🤖 New preprint: TinyHelen's First Curriculum: Training and Evaluating Tiny Language Models in a Simpler Language Environment 🤖 We train and evaluate tiny language models (LMs) using a novel text dataset with systematically simplified vocabularies and