Kathleen S. Meier-Hellstern

TL;DR: A 540-billion parameter, densely activated, Transformer language model, which is called PaLM achieves breakthrough performance, outperforming the state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark.

TL;DR: The purpose of this paper is to collect a number of useful results about Markov-modulated Poisson processes and queues with Markov -modulated input and to summary of recent developments.

TL;DR: The authors presented LaMDA: Language Models for Dialog Applications, a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters and are pre-trained on 1.56T words of public dialog data and web text and demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding.

TL;DR: The results of a measurement/modeling study focused on an office automation application on an ISDN are described and the measurements are used in the construction of a canonical model of the packet arrival process generated by a single user.

TL;DR: The PaLM 2 model as mentioned in this paper is a Transformer-based model trained using a mixture of objectives, which has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM.