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ThinkTank-ME: A Multi-Expert Framework for Middle East Event Forecasting

Haoxuan Li, He Chang, Yunshan Ma, Yi Bin, Yang Yang, See-Kiong Ng, Tat-Seng Chua

TL;DR

ThinkTank-ME tackles the limitations of single-LLM approaches for complex geopolitical event forecasting by simulating a think tank: multiple country-specialized experts coordinate via a leader model. It introduces POLECAT-FOR-ME, a Middle East–focused benchmark, and demonstrates that parameter-efficient fine-tuning of Llama-3.1-8B expert models combined with leader aggregation strategies (Expert Routing, Wisdom Aggregation, Elite Ensemble) yields superior accuracy over vanilla and all-data baselines. The Elite Ensemble variant offers the best balance between accuracy and efficiency, while analyses highlight the value and limits of country-resource distribution among proprietary models. Overall, the work provides a scalable framework for multi-expert, temporally grounded geopolitical forecasting with practical implications for policy analysis and risk assessment.

Abstract

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.

ThinkTank-ME: A Multi-Expert Framework for Middle East Event Forecasting

TL;DR

ThinkTank-ME tackles the limitations of single-LLM approaches for complex geopolitical event forecasting by simulating a think tank: multiple country-specialized experts coordinate via a leader model. It introduces POLECAT-FOR-ME, a Middle East–focused benchmark, and demonstrates that parameter-efficient fine-tuning of Llama-3.1-8B expert models combined with leader aggregation strategies (Expert Routing, Wisdom Aggregation, Elite Ensemble) yields superior accuracy over vanilla and all-data baselines. The Elite Ensemble variant offers the best balance between accuracy and efficiency, while analyses highlight the value and limits of country-resource distribution among proprietary models. Overall, the work provides a scalable framework for multi-expert, temporally grounded geopolitical forecasting with practical implications for policy analysis and risk assessment.

Abstract

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.
Paper Structure (11 sections, 1 equation, 3 figures, 1 table)

This paper contains 11 sections, 1 equation, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. ThinkTank-ME
  4. Dataset Construction
  5. Think Tank Forecasting: Expert Model
  6. Think Tank Forecasting: Leader Model
  7. Expert Routing.
  8. Wisdom Aggregation.
  9. Elite Ensemble.
  10. Experimental Results
  11. Conclusion and Future Work

Figures (3)

  • Figure 1: Illustration of the insufficiency of relying solely on a single LLM to analyze complex real-world events.
  • Figure 2: Schematic overview of the proposed ThinkTank-ME framework. This framework comprises multiple expert models, each specializing in a specific domain, coordinated by a leader model that synthesizes their diverse predictions. For the leader model, we investigate three distinct aggregation strategies: Expert Routing, Wisdom Aggregation, and Elite Ensemble.
  • Figure 3: Experimental validation of the expert and leader model. Subfigure (a) compares the No Training baseline with expert models on their country-specific test sets. Subfigure (b) shows the performance of the Elite Ensemble (Weighted Best-of-N) under varying numbers of selected experts and contrasts it with an untrained router. The $\star\ \&\ \triangle$ refer to the optimal and the Wisdom Aggregation.