The field of transformer research is moving towards improving efficiency and effectiveness in tokenization and attention mechanisms. Recent developments have focused on reducing computational overhead while maintaining performance, with techniques such as token reduction, merging, and freezing being explored. Additionally, there is a growing interest in understanding the underlying principles of self-attention and its applications in various domains. Notable papers in this area include those that propose innovative methods for tokenization, such as the Latent Denoising Tokenizer, and those that introduce new attention mechanisms, such as DistrAttention. These advancements have the potential to significantly impact the field of natural language processing and computer vision.
Efficient Tokenization and Attention Mechanisms in Transformers
Sources
Training-free Token Reduction for Vision Mamba
On the Effect of Token Merging on Pre-trained Models for Code
The Origin of Self-Attention: From Pairwise Affinity Matrices to Transformers
FOCUS: Fused Observation of Channels for Unveiling Spectra
Evaluation of Coding Schemes for Transformer-based Gene Sequence Modeling
Latent Denoising Makes Good Visual Tokenizers
Artifacts and Attention Sinks: Structured Approximations for Efficient Vision Transformers
ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference
DistrAttention: An Efficient and Flexible Self-Attention Mechanism on Modern GPUs
A Conditional Probability Framework for Compositional Zero-shot Learning
Attention (as Discrete-Time Markov) Chains
Hybrid Tokenization Strategy for DNA Language Model using Byte Pair Encoding and K-MER Methods