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

• URL: http://arxiv.org/abs/2310.01880v2
• Date: Thu, 18 Apr 2024 19:41:23 GMT
• Title: AutoCast++: Enhancing World Event Prediction with Zero-shot Ranking-based Context Retrieval
• Authors: Qi Yan, Raihan Seraj, Jiawei He, Lili Meng, Tristan Sylvain,
• Abstract summary: We introduce AutoCast++, a zero-shot ranking-based context retrieval system. Our approach first re-ranks articles based on zero-shot question-passage relevance, honing in on semantically pertinent news. We conduct both the relevance evaluation and article summarization without needing domain-specific training.
• Score: 9.357912396498142
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 rich subset of news snippets from a vast corpus. With this motivation, we introduce AutoCast++, a zero-shot ranking-based context retrieval system, tailored to sift through expansive news document collections for event forecasting. Our approach first re-ranks articles based on zero-shot question-passage relevance, honing in on semantically pertinent news. Following this, the chosen articles are subjected to zero-shot summarization to attain succinct context. Leveraging a pre-trained language model, we conduct both the relevance evaluation and article summarization without needing domain-specific training. Notably, recent articles can sometimes be at odds with preceding ones due to new facts or unanticipated incidents, leading to fluctuating temporal dynamics. To tackle this, our re-ranking mechanism gives preference to more recent articles, and we further regularize the multi-passage representation learning to align with human forecaster responses made on different dates. Empirical results underscore marked improvements across multiple metrics, improving the performance for multiple-choice questions (MCQ) by 48% and true/false (TF) questions by up to 8%. Code is available at https://github.com/BorealisAI/Autocast-plus-plus.

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