The field of natural language processing is moving towards more efficient models and algorithms, with a focus on reducing computational costs and memory requirements. Recent developments have introduced novel neural architecture search methods, such as Elastic Language Model, which optimize compact language models. Other innovations include the use of sparse attention, adaptive spans, and bilinear attention to improve text summarization. Additionally, researchers have proposed methods to optimize native sparse attention, such as Latent Attention and Local Global Alternating Strategies, to enhance long-context modeling. Noteworthy papers include Elastic Architecture Search for Efficient Language Models, which introduces a novel neural architecture search method, and BiSparse-AAS, which presents a framework for scalable and efficient text summarization. These advancements have the potential to significantly improve the performance and efficiency of natural language processing models.
Efficient Models and Algorithms for Natural Language Processing
Sources
Cross-Corpus Validation of Speech Emotion Recognition in Urdu using Domain-Knowledge Acoustic Features
Elastic Architecture Search for Efficient Language Models
BiSparse-AAS: Bilinear Sparse Attention and Adaptive Spans Framework for Scalable and Efficient Text Summarization
MISA: Memory-Efficient LLMs Optimization with Module-wise Importance Sampling
Language Modeling With Factorization Memory
H-FA: A Hybrid Floating-Point and Logarithmic Approach to Hardware Accelerated FlashAttention
Emotion Detection in Speech Using Lightweight and Transformer-Based Models: A Comparative and Ablation Study
FlashEVA: Accelerating LLM inference via Efficient Attention
Optimizing Native Sparse Attention with Latent Attention and Local Global Alternating Strategies
Memory-Efficient Training with In-Place FFT Implementation
mLR: Scalable Laminography Reconstruction based on Memoization
Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants
IG-Pruning: Input-Guided Block Pruning for Large Language Models
Digit-Recurrence Posit Division
Optimal Singular Damage: Efficient LLM Inference in Low Storage Regimes
ConMeZO: Adaptive Descent-Direction Sampling for Gradient-Free Finetuning of Large Language Models
TwIST: Rigging the Lottery in Transformers with Independent Subnetwork Training
HART: A Hybrid Addressing Scheme for Self-Balancing Binary Search Trees in Phase Change Memory (PCM)
Q3R: Quadratic Reweighted Rank Regularizer for Effective Low-Rank Training