Transformer-based architectures have significantly advanced the field of Information Retrieval (IR) by enabling semantic understanding that surpasses traditional term-frequency models. Hybrid approaches, which combine efficient sparse retrievers like BM25 with effective dense retrieval methods are becoming more popular for balancing performance and efficiency. However, the application of advanced hybrid systems often requires dedicated infrastructure and technical expertise, as existing toolkits are primarily research-oriented and not designed for simple integration.
To bridge this gap, this work introduces an open-source Elasticsearch plugin that implements the interpolation-based re-ranking framework from Leonhardt et al. This plugin simplifies the adoption of hybrid re-ranking by making the technique easily accessible within existing pipelines.
Furthermore, this work explores learning-based optimization methods to optimize the interpolation constant α. The research investigates two main strategies: (1) direct, gradient-based optimization to determine an optimized global value for α , and (2) the development of lightweight adaptor models that dynamically predict context-aware α values for each query or query-document pair. These adaptors are based on Feedforward Neural Networks and Neural Tensor Network architectures.
Empirical results suggest that these learning-based methods, particularly the dynamic adaptors, can outperform traditional grid search methods for tuning α, due to their dynamic adjustment in a query/document basis. The benefits were especially strong in out-of-domain scenarios, where the adaptors showed improved performance for encoders that were not pre-trained on the target domain, without requiring fine-tuning of the full backbone architecture.
This work delivers a practical, easy-to-use tool for hybrid re-ranking in Elasticsearch, a novel methodology for optimizing its core re-ranking parameter, and proposes adaptor models that can deliver better performance than a fixed interpolation value in hybrid re-ranking.