Mark Díaz

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 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: A novel framework is introduced, CrowdWorkSheets, for dataset developers to facilitate transparent documentation of key decisions points at various stages of the data annotation pipeline: task formulation, selection of annotators, platform and infrastructure choices, dataset analysis and evaluation, and dataset release and maintenance.

TL;DR: The authors argue that evaluations of offensive speech must focus on the impacts of language use, and they call this the cynic perspective, or a characteristic of language with roots in Cynic philosophy that pertains to employing offensive speech as a practice of resistance.

TL;DR: It was found that users broadly lacked relevant and helpful information to guide their assessments of translation quality, and design implications for explainable AI (XAI) researchers, MT researchers, as well as collaboration among them to support transparency and explainability in MT are proposed.