Counterfactual-based Cognitive Alignment In-Context Learning for Relation Extraction

Large Language Models (LLMs) have demonstrated remarkable In-Context learning (ICL) capabilities for relation extraction (RE). While ICL has shown promise in RE tasks, current approaches face challenges in example selection and utilization. These challenges stem from the misalignment between example selection methods and LLMs' inherent cognitive processing mechanisms, particularly in pattern recognition and relational reasoning. To address these limitations, we propose Counterfactual Cognitive Alignment (CCA), a novel framework that systematically enhances ICL performance in RE by aligning example selection with cognitive principles underlying human relational reasoning. The framework incorporates a cognitive-inspired counterfactual generation mechanism that creates semantically diverse yet relationally coherent examples, mirroring human "what-if" reasoning processes. Additionally, it employs a cognitive alignment approach that integrates structural identification features with semantic understanding to better align with LLMs cognitive processing patterns. Extensive experiments across multiple RE benchmarks reveal the effectiveness of our cognitive alignment approach through the synergistic integration of counterfactual reasoning and cognitively-guided selection.