ThinkTank-ME: A Multi-Expert Framework for Middle East Event Forecasting
This addresses the challenge of capturing diverse geopolitical nuances in Middle East event forecasting, which is incremental as it builds on existing LLM-based approaches by adding a multi-expert collaboration mechanism.
The authors tackled the problem of event forecasting in the Middle East by introducing ThinkTank-ME, a multi-expert framework that emulates collaborative expert analysis, and they constructed a new benchmark called POLECAT-FOR-ME for evaluation, showing superiority in handling complex temporal geopolitical forecasting tasks.
Event forecasting is inherently influenced by multifaceted considerations, including international relations, regional historical dynamics, and cultural contexts. However, existing LLM-based approaches employ single-model architectures that generate predictions along a singular explicit trajectory, constraining their ability to capture diverse geopolitical nuances across complex regional contexts. To address this limitation, we introduce ThinkTank-ME, a novel Think Tank framework for Middle East event forecasting that emulates collaborative expert analysis in real-world strategic decision-making. To facilitate expert specialization and rigorous evaluation, we construct POLECAT-FOR-ME, a Middle East-focused event forecasting benchmark. Experimental results demonstrate the superiority of multi-expert collaboration in handling complex temporal geopolitical forecasting tasks. The code is available at https://github.com/LuminosityX/ThinkTank-ME.