SPRING is an LLM-based policy that outperforms Reinforcement Learning algorithms in an interactive environment requiring multi-task planning and reasoning.  A group of researchers from Carnegie Mellon University, NVIDIA, Ariel University, and Microsoft have investigated the use of Large Language Models (LLMs) for understanding and reasoning with human knowledge in the context of games. They propose a two-stage approach called SPRING, which involves studying an academic paper and then using a Question-Answer (QA) framework to justify the knowledge obtained. More details about SPRING In the first stage, the authors read the LaTeX source code of the original paper by Hafner (2021)

In pre-trained transformer models (GPT), fine-tuning occurs in the Decoder. The decoder is responsible for generating the output text based on the representation created by the encoder. Like the encoder, the decoder is typically made up of multiple layers of multi-head self-attention and feed-forward neural networks.

Releasing 3B and 7B RedPajama-INCITE family of models including base, instruction-tuned and chat models.

Alpaca was developed on Meta AI's LLaMA 7B model and generated training data with a method known as self-instruct

I think one of the safest ways to move forward with this technology is to make sure that it is not in too few hands."

How neural networks build up their understanding of images

The current race towards ever larger "AI experiments" is not a preordained path where our only choice is how fast to run, but rather a set of decisions driven by the profit motive. The actions and choices of corporations must be shaped by regulation which protects the rights and interests of people.

OpenAI may have tested on the training data. Besides, human benchmarks are meaningless for bots.

Examples and guides for using the OpenAI API. Contribute to openai/openai-cookbook development by creating an account on GitHub.