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.
Artificial Intelligence
Natural Language Processing
Deep Learning
Machine Learning
Adaptive Systems
Published

January 18, 2025

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.

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