text: 240,46,1484373941078552579,96135022,Mark Riedl,"LaMDA paper is finally out. This is Google’s new big conversational dialogue model and successor to Meena <LINK> One of the more interesting things about it is that they use multi-task training to train it to generate language responses but also to generate knowledge retrieval queries. They train it on crowd workers using a fact checking tool. Like Meena, they use classifiers for inappropriate content to filter the dialogue training data and to filter generated outputs. They have spent considerable effort on this classifier. Overall, a huge engineering effort. Nothing terribly out of the ordinary for large language models at this particular moment of time (even incorporating retrieval into generative language models)",https://arxiv.org/abs/2201.08239,"We present LaMDA: Language Models for Dialog Applications. LaMDA is 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. While model scaling alone can improve quality, it shows less improvements on safety and factual grounding. We 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. The first challenge, safety, involves ensuring that the model's responses are consistent with a set of human values, such as preventing harmful suggestions and unfair bias. We quantify safety using a metric based on an illustrative set of human values, and we find that filtering candidate responses using a LaMDA classifier fine-tuned with a small amount of crowdworker-annotated data offers a promising approach to improving model safety. The second challenge, factual grounding, involves enabling the model to consult external knowledge sources, such as an information retrieval system, a language translator, and a calculator. We quantify factuality using a groundedness metric, and we find that our approach enables the model to generate responses grounded in known sources, rather than responses that merely sound plausible. Finally, we explore the use of LaMDA in the domains of education and content recommendations, and analyze their helpfulness and role consistency. ",LaMDA: Language Models for Dialog Applications,4,"['LaMDA paper is finally out. This is Google’s new big conversational dialogue model and successor to Meena <LINK>', 'One of the more interesting things about it is that they use multi-task training to train it to generate language responses but also to generate knowledge retrieval queries. They train it on crowd workers using a fact checking tool.', 'Like Meena, they use classifiers for inappropriate content to filter the dialogue training data and to filter generated outputs. They have spent considerable effort on this classifier.', 'Overall, a huge engineering effort. Nothing terribly out of the ordinary for large language models at this particular moment of time (even incorporating retrieval into generative language models)']",22,01,726