Towards Intrinsic Interpretability of Large Language Models:A Survey of Design Principles and Architectures
Survey of intrinsic interpretability architectures for LLMs; systematizes design principles for transparent model internals.
Search the full wire by company, model, lab, or keyword. Every story we have ever aggregated.
Survey of intrinsic interpretability architectures for LLMs; systematizes design principles for transparent model internals.
Learn what AI is, how it works, and how tools like ChatGPT use large language models. A clear, beginner-friendly guide to understanding artificial intelligence.
Deploying large language models (LLMs) requires large-scale distributed inference, which spreads model computation and request handling across many GPUs and... Deploying large language models (LLMs) requires large-scale distributed inference, which spreads model computation and…
Deploying and optimizing large language models (LLMs) for high-performance, cost-effective serving can be an overwhelming engineering problem. The ideal... Deploying and optimizing large language models (LLMs) for high-performance, cost-effective serving can be an overwhelming…
NVIDIA TensorRT LLM enables developers to build high-performance inference engines for large language models (LLMs), but deploying a new architecture... NVIDIA TensorRT LLM enables developers to build high-performance inference engines for large language models (LLMs), but…
Large language models (LLMs) are rapidly expanding their context windows, with recent models supporting sequences of 128K tokens, 256K tokens, and beyond.... Large language models (LLMs) are rapidly expanding their context windows, with recent models supporting sequences of…
In 2025, NVIDIA partnered with Black Forest Labs (BFL) to optimize the FLUX.1 text-to-image model series, unlocking FP4 image generation performance on NVIDIA... In 2025, NVIDIA partnered with Black Forest Labs (BFL) to optimize the FLUX.1 text-to-image model series, unlocking…
Large language models (LLMs) and multimodal reasoning systems are rapidly expanding beyond the data center. Automotive and robotics developers increasingly want... Large language models (LLMs) and multimodal reasoning systems are rapidly expanding beyond the data center…
Systematically evaluating the factuality of large language models with the FACTS Benchmark Suite.
Paper demonstrates alignment faking in LLMs: models selectively comply with training objectives while preserving original preferences without explicit training.
Google DeepMind introduces FACTS Grounding benchmark and leaderboard to measure LLM factuality and hallucination resistance.
Ollama is now available on Windows in preview, making it possible to pull, run and create large language models in a new native Windows experience. Ollama on Windows includes built-in GPU acceleration, access to the full model library, and serves the Ollama API including OpenAI…
We use GPT-4 to automatically write explanations for the behavior of neurons in large language models and to score those explanations. We release a dataset of these (imperfect) explanations and scores for every neuron in GPT-2.
OpenAI researchers collaborated with Georgetown University’s Center for Security and Emerging Technology and the Stanford Internet Observatory to investigate how large language models might be misused for disinformation purposes. The collaboration included an October 2021…
Cohere, OpenAI, and AI21 Labs have developed a preliminary set of best practices applicable to any organization developing or deploying large language models.
Call for expressions of interest to study the economic impacts of large language models.