Do Large Language Models Know Conflict? Investigating Parametric vs. Non-Parametric Knowledge of LLMs for Conflict Forecasting
This addresses the problem of early conflict warning for humanitarian and policy applications, but it is incremental as it builds on existing LLM and RAG methods.
The study investigated whether large language models (LLMs) can forecast violent conflict using only their pretrained parametric knowledge, compared to when augmented with external non-parametric data via retrieval-augmented generation (RAG), finding that external knowledge enhances performance for predicting conflict trends and fatalities in regions like the Horn of Africa and the Middle East from 2020-2024.
Large Language Models (LLMs) have shown impressive performance across natural language tasks, but their ability to forecast violent conflict remains underexplored. We investigate whether LLMs possess meaningful parametric knowledge-encoded in their pretrained weights-to predict conflict escalation and fatalities without external data. This is critical for early warning systems, humanitarian planning, and policy-making. We compare this parametric knowledge with non-parametric capabilities, where LLMs access structured and unstructured context from conflict datasets (e.g., ACLED, GDELT) and recent news reports via Retrieval-Augmented Generation (RAG). Incorporating external information could enhance model performance by providing up-to-date context otherwise missing from pretrained weights. Our two-part evaluation framework spans 2020-2024 across conflict-prone regions in the Horn of Africa and the Middle East. In the parametric setting, LLMs predict conflict trends and fatalities relying only on pretrained knowledge. In the non-parametric setting, models receive summaries of recent conflict events, indicators, and geopolitical developments. We compare predicted conflict trend labels (e.g., Escalate, Stable Conflict, De-escalate, Peace) and fatalities against historical data. Our findings highlight the strengths and limitations of LLMs for conflict forecasting and the benefits of augmenting them with structured external knowledge.