Summary of Forecasting Future International Events: a Reliable Dataset For Text-based Event Modeling, by Daehoon Gwak et al.
Forecasting Future International Events: A Reliable Dataset for Text-Based Event Modeling
by Daehoon Gwak, Junwoo Park, Minho Park, Chaehun Park, Hyunchan Lee, Edward Choi, Jaegul Choo
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to predicting future international events from textual information, such as news articles, is proposed by introducing WORLDREP (WORLD Relationship and Event Prediction), a large-scale dataset designed to leverage the advanced reasoning capabilities of large-language models. The dataset features high-quality scoring labels generated through prompt modeling and validated by domain experts in political science. Extensive experiments demonstrate the effectiveness of WORLDREP for real-world event prediction tasks, with implications for global policy, strategic decision-making, and geopolitics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary WORLDREP is a new way to predict future international events from news articles. This helps with making good decisions about what’s happening around the world. The problem is that existing datasets aren’t very good, so researchers can’t make progress. WORLDREP solves this by having high-quality labels generated using special prompts and checked by experts in politics. It also comes with code to help others collect, label, and test their own data. |
Keywords
» Artificial intelligence » Prompt
