On the Neural Hype and Improving Efficiency of Sparse Retrieval “The Neural Hype, Justified!” exclaimed Jimmy Lin in an opinion paper in the SIGIR Forum of December 2019. But is it really? Effectiveness-wise, maybe not: I will share some recent examples that show that neural rankers on new data do not even significantly improve a weak sparse baseline. If they do improve on old data, some neural rankers have been pre-trained on the test data – the ultimate sin of the machine learning professional – convincingly shown for the MovieLens data in the SIGIR 2025 poster of Dario Di Palma and colleagues: “Do LLMs Memorize Recommendation Datasets?” Efficiency-wise, neural rankers are no match to sparse rankers. The standard BERT (re-)ranker hailed by Lin’s SIGIR Forum paper may be as much as 10 million times as inefficient as a sparse ranker (Yes, you read that right). I will show some recent innovations for improving the efficiency of sparse rankers: The score-fitted index and the constant-length index (a SIGIR 2025 poster too!) which are implemented in Zoekeend, a new experimental search engine based on the relational database engine DuckDB and available from: https://gitlab.science.ru.nl/informagus/zoekeend/ _Presented at the SIGIR Workshop on Reaching Efficiency in Neural Information Retrieval (ReNeuIR 2025)_ [download slides]

My slides for the #ReNeuIR workshop at #SIGIR2025 are now at: djoerdhiemstra.com/2025/on-the-neural-hype-...

Rushing on your #ReNeuIR Workshop submissions? 📝 We have extended the deadline for another week! New deadline is May 22nd AoE. #SIGIR2024 ⏱️
reneuir.org/shared_task....