Large Language Models

OpenAI's new Rule-Based Rewards method improves AI safety without extensive human data collection. It uses simple rules to evaluate outputs, balancing helpfulness and safety in AI models.

NVIDIA launches four NeMo Retriever NIM microservices to improve LLM accuracy and performance. These tools enable efficient retrieval-augmented generation, connecting models to business data. They offer 30% fewer inaccurate answers in enterprise applications and integrate with various platforms.

Mistral AI and NVIDIA launch Mistral NeMo 12B, a 12-billion-parameter language model for enterprise use, excelling in diverse tasks and easy customisation.

Mistral AI releases Mathstral, a STEM-focused language model built on Mistral 7B. It excels in mathematical reasoning, achieving top performance on MATH and MMLU benchmarks for its size. The model is available for use and fine-tuning.

OpenAI's 'Prover-Verifier Games' method improves AI text clarity. It trains advanced AI to create outputs that simpler AI can easily check, enhancing human understanding.

Codestral Mamba offers linear time inference and infinite sequence handling. It excels in code generation and reasoning, matching top transformer models.