Trustworthiness of generative language models (GLMs) is crucial in their deployment to critical decision making systems.

While parameter-efficient fine-tuning (PEFT) methods aim to reduce the memory usage of the optimizer state during fine-tuning, the inherent size of pre-trained LLM weights continues to be a pressing concern. Even though quantization techniques are widely proposed to ease memory demands and accelerate LLM inference, most of these techniques are geared towards the deployment phase.

Natural language processing and 2D vision models have attained remarkable proficiency on many tasks primarily by escalating the scale of training data.

OpenAI tapped for voice control tech in U.S. drone swarm trial OpenAI has partnered with two defense technology companies that the Pentagon has selected to compete to develop voice-controlled, drone swarming software for the U.S. military, according to multiple people familiar with the matter. OpenAI has partnered with two defense technology companies that the Pentagon has selected to compete to develop voice-controlled, drone swarming software for the U.S. military, according to multiple people familiar with the matter. OpenAI's technology would only be used to translate voice commands from battlefield commanders to digital instructions for the drones, according to two of the people. It wouldn't be used for the operation of the drone swarm, weapons integration or targeting authority, the two people said. All of the people asked not to be named to discuss sensitive matters that aren't public.