Retrieval-Augmented Generation (RAG) has recently emerged as a promising solution to alleviate Large Language Model (LLM)'s deficiency in lack of knowledge. Existing RAG datasets, however, do not adequately represent the diverse and dynamic nature of real-world Question Answering (QA) tasks.

LLMs that exploit full model access remain largely unexplored.

The design is fun, creative, and totally impractical. Breakthroughs, discoveries, and DIY tips sent every weekday. Rotary phones, once a staple in the typical mid-century living room, were largely relegated to dusty basements and landfills following the introduction of the push-button touch-tone phone. But researchers at Google Japan have rewound the clock and imagined a world where the iconic spinning dial lived on, eventually evolving into a full-fledged mechanical keyboard. They've dubbed their whimsical, but utterly impractical, design the " Gboard Dial Version ."

Understanding the mechanisms of information storage and transfer in Transformer-based models is important for driving model understanding progress.

Particularly noteworthy is the 65.3% enhancement

Intent learning, which aims to learn users' intents for user understanding and