Enhancing Fusion-in-Decoder for Multi-Granularity Ranking

Large Language Models (LLMs) have demonstrated exceptional performance across various natural language tasks, leveraging extensive knowledge from massive datasets. However, their reliance solely on parametric knowledge often leads to the generation of inaccurate or outdated content, particularly in domain-specific tasks. Retrieval Augmented Generation (RAG) has emerged as a promising approach to address this limitation by incorporating external knowledge without necessitating re-training. While RAG enhances the accuracy of LLM-generated content, effectively retrieving external knowledge remains a challenge due to potential noise and computational costs. To address this, traditional information retrieval systems adopt two-stage approaches, utilizing efficient retrievers followed by reranking mechanisms. Recently, transformer-based architectures, including BERT and T5 models, have shown promise as effective rerankers. However, such models have limited context size and only perform single-granularity ranking at a time, hindering their effectiveness and efficiency. In this paper, we first explore the existing rerankers such as RankT5 and RFiD, highlighting challenges in multi-granularity ranking. Subsequently, we introduce PFiD (Passage Fusion-in-Decoder), a simple yet efficient approach aimed at effectively ranking both document and passage simultaneously. Through empirical evaluation, we demonstrate the efficacy of PFiD in improving effectiveness and efficiency, offering a promising direction for further research in this domain.