Correlation Between Attention Map Sparsity and Retrieval Recall in Domain-Adapted Dense Retrievers on ScienceQA

Dense retrievers have demonstrated significant potential for neural information retrieval; however, they exhibit a lack of robustness to domain shifts, thereby limiting their efficacy in zero-shot settings across diverse domains. Previous research has investigated unsupervised domain adaptation techniques to adapt dense retrievers to target domains. However, these studies have not focused on explainability analysis to understand how such adaptations alter the model's behavior. In this paper, we propose utilizing the integrated gradients framework to develop an interpretability method that prov

Research goal: What is the correlation between attention map sparsity in domain-adapted dense retrievers and their retrieval recall metrics on the ScienceQA multimodal benchmark?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.3/10.