Large Language Models: A New Moore's Law?

Understanding the capabilities, limitations, and societal impact of large language models

Very Large Language Models and How to Evaluate Them

Scaling laws for neural language models

On the Smallness of the Large Language Models Scaling Exponents

arXiv:2606. 24504v1 Announce Type: new Abstract: We discuss reasons why the scaling exponents of current Large Language Models (LLMs) applications are indicating an unsustainable regime in terms of energy resources.

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.

Phase transition in large language models and the criticality of natural languages

arXiv:2406. 05335v3 Announce Type: replace-cross Abstract: Generation of text and speech in natural languages can be modeled as a stochastic process.

Block Sparse Matrices for Smaller and Faster Language Models

Introducing The World's Largest Open Multilingual Language Model: BLOOM

Falcon-Edge: A series of powerful, universal, fine-tunable 1.58bit language models.

The Reformer - Pushing the limits of language modeling

Towards Encrypted Large Language Models with FHE

Register Bias in Complexity-Based Large Language Model Routing

The paper examines how large language model (LLM) services route queries to models of varying size based on a cheap complexity estimate. It finds that this routing is not register neutral: queries written in non‑standard English registers (e.g., African American English or second‑language English) are systematically assigned to lower‑capacity models because they appear shorter due to omitted function words. Experiments on 37,704 learner sentence pairs and a controlled corpus show that this bias leads to significantly lower accuracy across all model tiers, including the highest‑capacity cloud models, while the routing decision itself adds little marginal cost.