Transformer^2: Self-adaptive LLMs
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.
6785e78b117627fe712005c9
Qi Sun
claimed_verified
2025-01-14T09:11:43.467Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/644b983f0fbe4830f192c4f5/4eX4OS0gnXGqLu3eaMml_.png
Qi Sun
user
lfsm
6785e78b117627fe712005ca
Edoardo Cetin
admin_assigned
2025-01-14T09:07:24.416Z
65ba6db23a03c2490a342647
/avatars/92221b08e679b2944c9c78f079da9fc6.svg
Edoardo Cetin
user
edoarc
6785e78b117627fe712005cb
Yujin Tang
claimed_verified
2025-01-14T09:09:55.298Z
63285beda4102056bc198395
/avatars/6c936818a3814c1c32a8eaae8b8cb6d6.svg
yujin tang
user
tyj2022
Transformer^2: Self-adaptive LLMs
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.
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.
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