Transformer^2: Self-adaptive LLMs
Authors: name: Qi Sun, name: Edoardo Cetin, name: Yujin Tang

Self-adaptive large language models (LLMs) aim to solve the challenges posed
by traditional fine-tuning methods, which are often computationally intensive
and static in their ability to handle diverse tasks. We introduce \implname, a
novel self-adaptation framework that adapts LLMs for unseen tasks in real-time
by selectively adjusting only the singular components of their weight matrices.
During inference, \implname employs a two-pass mechanism: first, a dispatch
system identifies the task properties, and then task-specific "expert" vectors,
trained using reinforcement learning, are dynamically mixed to obtain targeted
behavior for the incoming prompt. Our method outperforms ubiquitous approaches
such as LoRA, with fewer parameters and greater efficiency. \implname
demonstrates versatility across different LLM architectures and modalities,
including vision-language tasks. \implname represents a significant leap
forward, offering a scalable, efficient solution for enhancing the adaptability
and task-specific performance of LLMs, paving the way for truly dynamic,
self-organizing AI systems.