Recent advances in large language models have enabled the development of viable generative information retrieval systems. A generative retrieval system returns a grounded generated text in response to an information need instead of the traditional document ranking.

Awarded Best Paper Honorable Mention! Recent advances in Information Retrieval utilise energy-intensive hardware to produce state-of-the-art results. In areas of research highly related to Information Retrieval, such as Natural Language Processing and Machine Learning, there have been efforts to quantify and reduce the power and emissions produced by methods that depend on such hardware.