CONTEXT-AWARE ADAPTATION OF GENERATIVE LLM RESPONSES

In crisis situations, the speed of response becomes critically important. Large Language Models (LLMs) are capable of generating useful recommendations; however, their default behavior often fails to account for the specific context of emergency events. This paper proposes the Adaptive Injectable Realignment Model (Adaptive IRM) as a method for achieving context-aware response generation. Adaptive IRM is a lightweight neural module that integrates into the LLM’s forward pass and injects contextual signals to adjust the model’s internal representations without modifying its original weights. Our approach is focused on disaster scenarios, using N neurons (in our case, N = 4) corresponding to natural hazards such as earthquakes, floods, fires, and hurricanes. The output signals from the Adaptive IRM, injected at various transformer layers, modulate attention mechanisms to emphasize information relevant to the given context. The paper outlines the IRM architecture, describes the proposed extensions, and presents a plan for using the HumAID dataset to train the Adaptive IRM. Experimental results are not yet available; instead, the concept, motivation, and implementation strategy of the proposed system are discussed.