Perhaps the applied nature of information retrieval (IR) research goes some way to explain the community’s rich history of evaluating machine learning models holistically, understanding that efficacy matters but so does the computational cost incurred to achieve it. This is evidenced by, among other efforts, more than a decade of research on efficient training and inference of large decision forest models in learning-to-rank. The community systematically investigated questions of efficiency and explored the trade-offs between efficiency and effectiveness in ranking models, leading to several innovations such as multi-stage architectures, cost-aware training and pruning algorithms, early-exit strategies, and fast decision forest inference algorithms.
As IR adopts even more complex, neural network-based models in a wide range of applications—marking the beginning of a new era known as Neural Information Retrieval (NIR)—questions on efficiency have once again become relevant. Whatever the reason behind their success may be, NIR models achieve a greater effectiveness than the previous wave of machine learning models like decision forests on many IR tasks, but with orders of magnitude more learnable parameters and much greater amounts of data. Achieving high accuracy by way of ever-increasing complexity once again presents a new, but nonetheless familiar challenge that necessitates the exploration of the Pareto frontier of the two competing objectives: efficiency and effectiveness—echoes of the past decade of research albeit in a different context.
While researchers in various communities concurrently investigate efficiency-related questions posed by neural network-based methods, we believe that the IR community would benefit from an organized effort that is focused on NIR. We therefore hope that this workshop on Reaching Efficiency in Neural Information Retrieval (ReNeuIR) will serve as a forum for a critical discussion of efficiency in the era of NIR. Our goal is to encourage debate on the current state and future directions of research in this space, and to promote more sustainable research by identifying best practices in the development and evaluation of neural models for IR.