Red-Teaming Large Language Models

Very Large Language Models and How to Evaluate Them

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

Evaluating large language models trained on code

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

The Reformer - Pushing the limits of language modeling

Efficient training of language models to fill in the middle

Advancing red teaming with people and AI

Advancing red teaming with people and AI

Scaling laws for neural language models

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.

Stable-GFlowNet: Toward Diverse and Robust LLM Red-Teaming via Contrastive Trajectory Balance

arXiv:2605. 00553v3 Announce Type: replace Abstract: Large Language Model (LLM) Red-Teaming, which proactively identifies vulnerabilities of LLMs, is an essential process for ensuring safety.

Accelerating Vision-Language Models: BridgeTower on Habana Gaudi2

ARENA: Automated Red-Teaming for Large Audio Language Models

arXiv:2608. 15578v1 Announce Type: cross Abstract: Large audio-language models (LALMs) make it possible to interact with language models through speech, music, and environmental sound, but they also introduce a safety surface that is difficult to expose with text-only red-teaming.