Transformer2: Self-Adaptive Large Language Models

The paper discusses the development of Transformer2, a framework for self-adaptive Large Language Models (LLMs), introducing a novel parameter-efficient fine-tuning method called Singular Value Fine-tuning (SVF). The paper explores three distinct adaptation strategies within Transformer2 and evaluates its performance on various tasks and datasets.

Key takeaways are that SVF outperforms traditional fine-tuning methods like LoRA in efficiency, flexibility, and robustness. The paper also introduces innovative adaptation strategies like Few-Shot Adaptation using the Cross-Entropy Method, showcasing the effectiveness of the Transformer2 framework in adaptive AI systems.

Read full paper: https://arxiv.org/abs/2501.06252

Tags: Artificial Intelligence, Natural Language Processing, Deep Learning, Machine Learning, Adaptive Systems