Query Performance Prediction Using Neural Query Space Proximity

The varying performance of information retrieval (IR) methods, including state-of-the-art transformer-based neural retrievers, across diverse queries poses a significant challenge for achieving robust and reliable retrieval effectiveness. Query Performance Prediction (QPP) seeks to estimate the effectiveness of a retrieval method for individual queries, enabling adaptive strategies to improve retrieval outcomes, particularly for challenging queries. However, existing QPP approaches face fundamental challenges: pre-retrieval methods often rely on surface-level query features that fail to capture the nuanced relationship between queries and retrieval effectiveness, while post-retrieval methods depend heavily on the quality of retrieved documents, which can be unreliable for difficult queries. To this end, we propose the Query Space Distance-Based QPP ( QSD-QPP ) framework, which leverages the deterministic and consistent behavior of retrieval methods to estimate query performance by referencing historical queries with known effectiveness. The approach is motivated by the observation that semantically or syntactically similar queries often exhibit consistent retrieval performance, a property that can be exploited to make reliable predictions for unseen queries. QSD-QPP operates in two modes: (1) a lightweight pre-retrieval instantiation that dynamically constructs a query subspace based on embedding distances to interpolate the performance of proximate historical queries, and (2) an enriched post-retrieval instantiation that incorporates contextualized embeddings, document interactions, and historical query associations to enhance prediction accuracy. By utilizing large-scale contextualized embeddings derived from pre-trained language models, QSD-QPP efficiently identifies semantically similar queries and leverages their performance for robust predictions. By addressing the inherent limitations of prior approaches, QSD-QPP achieves a balanced trade-off between computational efficiency, prediction accuracy, and scalability. We evaluate QSD-QPP on four benchmark datasets, including MS MARCO Dev and TREC Deep Learning tracks (2019, 2020, and DL-Hard), demonstrating its superior accuracy and robustness compared to state-of-the-art baselines in both pre-retrieval and post-retrieval QPP tasks. To ensure reproducibility and encourage further research, we publicly release the implementation of our work.