AutoCast++: Enhancing World Event Prediction with Zero-shot Ranking-based Context Retrieval

arXiv:2310.01880v2 Announce Type: replace
Abstract: Machine-based prediction of real-world events is garnering attention due to its potential for informed decision-making. Whereas traditional forecasting predominantly hinges on structured data like time-series, recent breakthroughs in language models enable predictions using unstructured text. In particular, (Zou et al., 2022) unveils AutoCast, a new benchmark that employs news articles for answering forecasting queries. Nevertheless, existing methods still trail behind human performance. The cornerstone of accurate forecasting, we argue, lies in identifying a concise, yet …

arxiv context cs.lg event prediction ranking retrieval type world zero-shot