CybAI news

arXiv:2405.17455v1 Announce Type: new Abstract: This paper introduces WeatherFormer, a transformer encoder-based model designed to learn robust weather features from minimal observations. It addresses the challenge of modeling complex weather dynamics from small datasets, a bottleneck for many prediction tasks in agriculture, epidemiology, and climate science. WeatherFormer was pretrained on a large pretraining dataset comprised of 39 years of satellite measurements across the Americas. With a novel pretraining task and fine-tuning, WeatherFormer achieves state-of-the-art performance in county-level soybean yield prediction and influenza forecasting. Technical innovations include a unique spatiotemporal encoding that captures geographical, annual, and seasonal variations, adapting the transformer…

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol

Over the last few years, Large Language Models (LLMs) have been reshaping the field of natural language, thanks to their transformer-based architectures and their extensive training on massive datasets.In particular, Retrieval Augmented Generation (RAG) has experienced a notable rise, swiftly becoming the prevailing method for effectively exploring and retrieving enterprise data by combining vector databases and LLMs. Some of its common applications involve developing customer support bots, internal knowledge graphs, or Q&A systems.This tremendous progress, however, has also given rise to various challenges, with one of the most prominent being the complicated task of testing and validating their generated outputs.How…