Advancements in Large Language Models

The field of large language models (LLMs) is rapidly evolving, with a focus on improving their performance, efficiency, and adaptability. Recent developments have explored innovative architectures, training methods, and optimization techniques to enhance the capabilities of LLMs. Notably, researchers have investigated the use of byte-level modeling, isotropy, and stochastic depth training to improve the accuracy and robustness of LLMs. Additionally, there is a growing interest in understanding the effects of sample training orders, learning rates, and weight decay on the performance of LLMs. These advancements have the potential to significantly impact the field of natural language processing and beyond.

Some noteworthy papers in this area include: L-MTP, which proposes a leap multi-token prediction method to improve the efficiency and accuracy of LLMs. DASH, which introduces an adaptive layer-skipping framework to reduce the inference cost of LLMs. NeuroTrails, which presents a sparse multi-head architecture to improve the performance and robustness of LLMs. EnsemW2S, which proposes a novel method for enhancing weak-to-strong generalization in LLMs.

Sources

BanglaByT5: Byte-Level Modelling for Bangla

When can isotropy help adapt LLMs' next word prediction to numerical domains?

Next Token Perception Score: Analytical Assessment of your LLM Perception Skills

The Rise of Parameter Specialization for Knowledge Storage in Large Language Models

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies

L-MTP: Leap Multi-Token Prediction Beyond Adjacent Context for Large Language Models

Leveraging Stochastic Depth Training for Adaptive Inference

Scaling Recurrent Neural Networks to a Billion Parameters with Zero-Order Optimization

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling

Rethinking the Outlier Distribution in Large Language Models: An In-depth Study

In Search of Adam's Secret Sauce

Taming Transformer Without Using Learning Rate Warmup

EnsemW2S: Enhancing Weak-to-Strong Generalization with Large Language Model Ensembles

Learning in Compact Spaces with Approximately Normalized Transformers

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining

On the Surprising Effectiveness of Large Learning Rates under Standard Width Scaling

Benignity of loss landscape with weight decay requires both large overparametrization and initialization

Pre-Training Curriculum for Multi-Token Prediction in Language Models

REOrdering Patches Improves Vision Models