The aim of this Special Section is to engage with researchers in Information Retrieval, Natural Language Processing and related areas and gather insight into the core challenges in measuring, reporting, and optimizing all facets of efficiency in Neural Information Retrieval (NIR) systems, including time-, space-, resource-, sample-, and energy-efficiency, among other factors. While researchers in the field have assiduously explored the Pareto frontier in quality and efficiency in other contexts for decades, we believe that the neural dimension introduces new hurdles.
Special Section on Efficiency in Neural Information Retrieval
Bruch S.;Lucchese C.;
2024-01-01
Abstract
The aim of this Special Section is to engage with researchers in Information Retrieval, Natural Language Processing and related areas and gather insight into the core challenges in measuring, reporting, and optimizing all facets of efficiency in Neural Information Retrieval (NIR) systems, including time-, space-, resource-, sample-, and energy-efficiency, among other factors. While researchers in the field have assiduously explored the Pareto frontier in quality and efficiency in other contexts for decades, we believe that the neural dimension introduces new hurdles.File in questo prodotto:
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