The field of natural language processing is moving towards more efficient training and adaptation of large language models. Researchers are exploring innovative methods to accelerate the training process, such as multilevel approaches and adaptive expert replication. These methods aim to reduce computational overhead and improve convergence rates. Another area of focus is the development of parameter-efficient fine-tuning methods, which enable the adaptation of pre-trained models to new tasks without requiring large amounts of additional training data. Notable papers in this area include Accelerating Mixture-of-Experts Training with Adaptive Expert Replication, which introduces SwiftMoE, a system that achieves faster time-to-convergence compared to state-of-the-art MoE training systems. Additionally, Efficient Knowledge Transfer in Multi-Task Learning through Task-Adaptive Low-Rank Representation proposes a method called Task-Adaptive Low-Rank Representation (TA-LoRA) that achieves state-of-the-art performance in full-data and few-shot settings while maintaining superior parameter efficiency. Mixture of Sparse Attention: Content-Based Learnable Sparse Attention via Expert-Choice Routing is also noteworthy, as it presents a novel approach to reducing the computational complexity of self-attention mechanisms.
Efficient Training and Adaptation of Large Language Models
Sources
A multilevel approach to accelerate the training of Transformers
Accelerating Mixture-of-Experts Training with Adaptive Expert Replication
Partial Answer of How Transformers Learn Automata
FT-MoE: Sustainable-learning Mixture of Experts Model for Fault-Tolerant Computing with Multiple Tasks
TT-LoRA MoE: Unifying Parameter-Efficient Fine-Tuning and Sparse Mixture-of-Experts
Efficient Knowledge Transfer in Multi-Task Learning through Task-Adaptive Low-Rank Representation
Mixture of Sparse Attention: Content-Based Learnable Sparse Attention via Expert-Choice Routing
Investigating Task Arithmetic for Zero-Shot Information Retrieval