Lora Aroyo

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: Investigating the influence of transparency on user trust in and acceptance of content-based recommender systems in the cultural heritage domain shows that explaining to the user why a recommendation was made increased acceptance of the recommendations, but trust in the system itself was not improved by transparency.

TL;DR: The path towards a more open, connected and smart cultural heritage is shown: open (the data is open, shared and accessible), connected ( the use of linked data allows for interoperable infrastructures, with users and providers getting more and more connected), and smart (the use of knowledge and web technologies allows us to provide interesting data to the right users).

TL;DR: The Semantic Web - ISWC 2011 - 10th ISWC Conference, Bonn, Germany, October 23-27, 2011, Proceedings, Part I as discussed by the authors, Part I