The introduction of large language models (LLMs) provides a language interface for robots, allowing for easier task descriptions and enabling robots to perform generic tasks more effectively.

Large Language Models (LLMs) function like a new operating system, being developed by labs and distributed like utilities, reminiscent of computing in the 1960s.

Large language models (LLMs) are primarily designed to regurgitate information based on statistical patterns in the text they've been trained on, which limits their ability to invent new concepts or solutions.

The breakthrough with large language models (LLMs) has accelerated the development of high-quality AI, which is now advancing faster than holographic AR technology.

We've entered the era of 'Software 3.0,' where large language models (LLMs) serve as a new kind of computer, programmed in English.

Many mundane tasks in information processing are perfect for automation using large language models, yet there are few applications targeting this area.

The development of large language models (LLMs) like GPT-2 marked a significant turning point for OpenAI, showcasing the potential of AI.

The new technology of large language models is creating unprecedented opportunities for startups, enabling tasks that previously required extensive research teams to be accomplished more easily.

Reinforcement learning is integrated into large language models by combining it with human data, which contrasts with the self-learning approach of AlphaZero, and this reinforcement learning from human feedback (RLHF) has transformed large language models (LLMs) from systems that merely mimic data into ones that produce useful answers to questions.