Block Sparse Matrices for Smaller and Faster Language Models

Very Large Language Models and How to Evaluate Them

Efficient training of language models to fill in the middle

Large Language Models: A New Moore's Law?

Break Through the Compression Bottleneck: From Theory to Practice

arXiv:2607. 20434v1 Announce Type: cross Abstract: As the parameter size of language models continues to grow, effective model compression is required to reduce their computational and memory overhead.

Riemannian Gradient Descent for Low-Rank Architectures

arXiv:2606. 02328v1 Announce Type: new Abstract: We explore Riemannian optimization techniques for rank-factored matrix parameters, targeting contemporary deep learning applications.

Evaluating large language models trained on code

The State Of LLMs 2025: Progress, Problems, and Predictions

A 2025 review of large language models, from DeepSeek R1 and RLVR to inference-time scaling, benchmarks, architectures, and predictions for 2026.

Databricks ❤️ Hugging Face: up to 40% faster training and tuning of Large Language Models

The Reformer - Pushing the limits of language modeling

Adaptive Block Diffusion: Resolving Training-Inference Mismatch in Diffusion Language Models

arXiv:2606. 29275v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) are typically trained under fixed context structures, restricting denoising to predetermined token subsets.

BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning

arXiv:2608. 05104v1 Announce Type: new Abstract: Deep neural networks have shown impressive success in NLP tasks owing to their complex structure and huge number of edges.

Attention-Based Sampler for Diffusion Language Models

arXiv:2604. 08564v2 Announce Type: replace-cross Abstract: Auto-regressive models (ARMs) have established a dominant paradigm in language modeling.