TY - GEN
T1 - SIGIR 2023 Workshop on Retrieval Enhanced Machine Learning (REML @ SIGIR 2023)
AU - Bendersky, Michael
AU - Diaz, Fernando
AU - Chen, Danqi
AU - Zamani, Hamed
N1 - Publisher Copyright:
© 2023 Copyright held by the owner/author(s).
PY - 2023/7/19
Y1 - 2023/7/19
N2 - Most machine learning models are designed to be self-contained and encode both “knowledge” and “reasoning” in their parameters. However, such models cannot perform effectively for tasks that require knowledge grounding and tasks that deal with non-stationary data, such as news and social media. Besides, these models often require huge number of parameters to encode all the required knowledge. These issues can be addressed via augmentation with a retrieval model. This category of machine learning models, which is called Retrieval-enhanced machine learning (REML), has recently attracted considerable attention in multiple research communities. For instance, REML models have been studied in the context of open-domain question answering, fact verification, and dialogue systems and also in the context of generalization through memorization in language models and memory networks. We believe that the information retrieval community can significantly contribute to this growing research area by designing, implementing, analyzing, and evaluating various aspects of retrieval models with applications to REML tasks. The goal of this full-day hybrid workshop is to bring together researchers from industry and academia to discuss various aspects of retrieval-enhanced machine learning, including effectiveness, efficiency, and robustness of these models in addition to their impact on real-world applications.
AB - Most machine learning models are designed to be self-contained and encode both “knowledge” and “reasoning” in their parameters. However, such models cannot perform effectively for tasks that require knowledge grounding and tasks that deal with non-stationary data, such as news and social media. Besides, these models often require huge number of parameters to encode all the required knowledge. These issues can be addressed via augmentation with a retrieval model. This category of machine learning models, which is called Retrieval-enhanced machine learning (REML), has recently attracted considerable attention in multiple research communities. For instance, REML models have been studied in the context of open-domain question answering, fact verification, and dialogue systems and also in the context of generalization through memorization in language models and memory networks. We believe that the information retrieval community can significantly contribute to this growing research area by designing, implementing, analyzing, and evaluating various aspects of retrieval models with applications to REML tasks. The goal of this full-day hybrid workshop is to bring together researchers from industry and academia to discuss various aspects of retrieval-enhanced machine learning, including effectiveness, efficiency, and robustness of these models in addition to their impact on real-world applications.
KW - Knowledge Grounding
KW - Memory-Augmented Networks
KW - Retrieval Augmentation
UR - http://www.scopus.com/inward/record.url?scp=85164665850&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85164665850&partnerID=8YFLogxK
U2 - 10.1145/3539618.3591925
DO - 10.1145/3539618.3591925
M3 - Conference contribution
AN - SCOPUS:85164665850
T3 - SIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
SP - 3468
EP - 3471
BT - SIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
PB - Association for Computing Machinery, Inc
T2 - 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023
Y2 - 23 July 2023 through 27 July 2023
ER -