Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities

• URL: http://arxiv.org/abs/2503.14802v1
• Date: Wed, 19 Mar 2025 00:28:54 GMT
• Title: Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities
• Authors: Md Shahir Zaoad, Niamat Zawad, Priyanka Ranade, Richard Krogman, Latifur Khan, James Holt,
• Abstract summary: Graph Neural Networks (GNNs) have been proposed to demonstrate proficiency in graph learning for re-ranking.<n>There are ongoing limitations in modeling and evaluating input graph structures for training and evaluation.
• Score: 14.132502540570403
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Knowledge graphs have emerged to be promising datastore candidates for context augmentation during Retrieval Augmented Generation (RAG). As a result, techniques in graph representation learning have been simultaneously explored alongside principal neural information retrieval approaches, such as two-phased retrieval, also known as re-ranking. While Graph Neural Networks (GNNs) have been proposed to demonstrate proficiency in graph learning for re-ranking, there are ongoing limitations in modeling and evaluating input graph structures for training and evaluation for passage and document ranking tasks. In this survey, we review emerging GNN-based ranking model architectures along with their corresponding graph representation construction methodologies. We conclude by providing recommendations on future research based on community-wide challenges and opportunities.

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