eXplainable Bayesian Multi-Perspective Generative Retrieval

Modern deterministic retrieval pipelines prioritize achieving state-of-the-art performance but often lack interpretability in decision-making. These models face challenges in assessing uncertainty, leading to overconfident predictions. To overcome these limitations, we integrate uncertainty calibration and interpretability into a retrieval pipeline. Specifically, we introduce Bayesian methodologies and multi-perspective retrieval to calibrate uncertainty within a retrieval pipeline. We incorporate techniques such as LIME and SHAP to analyze the behavior of a black-box reranker model. The importance scores derived from these explanation methodologies serve …

art bayesian challenges cs.ir cs.lg decision face generative generative retrieval interpretability limitations making modern performance perspective pipeline pipelines predictions retrieval state uncertainty