title: Transformer^2: Self-adaptive LLMs
summary: 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.
paper_authors._id: 6785e78b117627fe712005c9
paper_authors.name: Qi Sun
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-14T09:11:43.467Z
paper_authors._id: 644b983f0fbe4830f192c4f5
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/644b983f0fbe4830f192c4f5/4eX4OS0gnXGqLu3eaMml_.png
paper_authors.fullname: Qi Sun
paper_authors.type: user
paper_authors.user: lfsm
paper_authors._id: 6785e78b117627fe712005ca
paper_authors.name: Edoardo Cetin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-14T09:07:24.416Z
paper_authors._id: 65ba6db23a03c2490a342647
paper_authors.avatarUrl: /avatars/92221b08e679b2944c9c78f079da9fc6.svg
paper_authors.fullname: Edoardo Cetin
paper_authors.type: user
paper_authors.user: edoarc
paper_authors._id: 6785e78b117627fe712005cb
paper_authors.name: Yujin Tang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-14T09:09:55.298Z
paper_authors._id: 63285beda4102056bc198395
paper_authors.avatarUrl: /avatars/6c936818a3814c1c32a8eaae8b8cb6d6.svg
paper_authors.fullname: yujin tang
paper_authors.type: user
paper_authors.user: tyj2022
paper_title: Transformer^2: Self-adaptive LLMs
paper_summary: 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.
paper_ai_summary: A self-adaptive framework for large language models uses reinforcement learning to dynamically adjust task-specific components during inference, enhancing adaptability and performance with efficiency.
paper_ai_keywords: self-adaptive large language models (LLMs) fine-tuning weight matrices dispatch system expert vectors reinforcement learning LoRA vision-language tasks scalability self-organizing AI systems