Jasmine Wang

TL;DR: This report discusses OpenAI's work related to the release of its GPT-2 language model and discusses staged release, which allows time between model releases to conduct risk and benefit analyses as model sizes increased.

TL;DR: This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems and their associated development processes, with a focus on providing evidence about the safety, security, fairness, and privacy protection of AI systems.

TL;DR: This work proposes an unreferenced automated evaluation metric that uses large pre-trained language models to extract latent representations of utterances, and leverages the temporal transitions that exist between them and shows that the model achieves higher correlation with human annotations in an online setting, while not requiring true responses for comparison during inference.

TL;DR: The authors proposed an unreferenced automated evaluation metric that uses large pre-trained language models to extract latent representations of utterances, and leverages the temporal transitions that exist between them.