ConfliLPC: Logits and Parameter Calibration for Political Conflict Analysis in Continual Learning

The ConfliLPC framework introduces an innovative integration of Logits and Parameter Calibration (LPC) with the ConfliBERT model, tailored specifically for the nuanced analysis of political conflict and violence. This paper details the development and application of ConfliLPC, highlighting its robust capability to adapt to evolving data landscapes without succumbing to catastrophic forgetting (CF), a common challenge in machine learning models applied to dynamic domains such as political science. ConfliLPC enhances accuracy and adaptability by continually adjusting its parameters to accommodate new information while retaining valuable historical insights. The framework has been rigorously tested across various conflict scenarios, demonstrating superior performance in real-time analysis and predictive tasks. This work serves as a significant contribution to the fields of political science, conflict research, and applied machine learning, providing a powerful tool for analysts and policymakers engaged in the understanding and resolution of political conflict. The experimental results highlight the efficiency of the ConfliLPC method and its capability to minimize CF. Our code is publicly available<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>