Future Language Modeling from Temporal Document History

arXiv:2404.10297v1 Announce Type: new
Abstract: Predicting the future is of great interest across many aspects of human activity. Businesses are interested in future trends, traders are interested in future stock prices, and companies are highly interested in future technological breakthroughs. While there are many automated systems for predicting future numerical data, such as weather, stock prices, and demand for products, there is relatively little work in automatically predicting textual data. Humans are interested in textual data predictions because it is …

abstract arxiv automated businesses companies cs.ai cs.cl data document future history human language modeling numerical stock systems temporal traders trends type