The Answer Lies Within: Self-Derived Rewards Enable Explainable Relation Extraction
The framework introduces COGRE, a cognitively-inspired reasoning structure, and HIT@DICT, an intermediate reinforcement learning reward strategy to enable explainable relation extraction in large language models. This addresses attention misalignment and granularity mismatches by mimicking human text processing steps. Experiments show improved accuracy and explanation quality.