Arabzadeh, N., & Clarke, C. (2024). A Comparison of Methods for Evaluating Generative IR ArXiv, abs/2404.04044. https://doi.org/10.48550/ARXIV.2404.04044
References
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2024
Hebert, L., Sahu, G., Guo, Y., Sreenivas, N. K., Golab, L., & Cohen, R. (2024). Multi-Modal Discussion Transformer: Integrating Text, Images and Graph Transformers to Detect Hate Speech on Social Media Presented at the Multi-Modal Discussion Transformer: Integrating Text, Images and Graph Transformers to Detect Hate Speech on Social Media conference. https://doi.org/10.1609/AAAI.V38I20.30213
Faggioli, G., Dietz, L., Clarke, C., Demartini, G., Hagen, M., Hauff, C., … Wachsmuth, H. (2024). Who Determines What Is Relevant? Humans or AI? Why Not Both? Communications of the ACM, 67, 31-34. https://doi.org/10.1145/3624730
Usta, A., Liu, C., & Salihoglu, S. (2024). Analysis of Open Government Datasets From a Data Design and Integration Perspective Presented at the Analysis of Open Government Datasets From a Data Design and Integration Perspective conference. https://doi.org/10.48786/EDBT.2024.30
Lin, J., Li, J., Gao, J., Ma, W., & Liu, Y. (2024). Jointly Modeling Spatio-Temporal Features of Tactile Signals for Action Classification Presented at the Jointly Modeling Spatio-Temporal Features of Tactile Signals for Action Classification conference. https://doi.org/10.1609/AAAI.V38I12.29288
Upadhyay, S., Kamalloo, E., & Lin, J. (2024). LLMs Can Patch Up Missing Relevance Judgments in Evaluation ArXiv, abs/2405.04727. https://doi.org/10.48550/ARXIV.2405.04727
Zhang, X., Ogueji, K., Ma, X., & Lin, J. (2024). Toward Best Practices for Training Multilingual Dense Retrieval Models ACM Transactions on Information Systems (TOIS), 42, 1-39. https://doi.org/10.1145/3613447
Azzopardi, L., Clarke, C., Kantor, P. B., Mitra, B., Trippas, J. R., & Ren, Z. (2024). The Search Futures Workshop Presented at the The Search Futures Workshop conference. https://doi.org/10.1007/978-3-031-56069-9_57
Arabzadeh, N., & Clarke, C. (2024). Fr\ echet Distance for Offline Evaluation of Information Retrieval Systems With Sparse Labels Presented at the Fr\ Echet Distance for Offline Evaluation of Information Retrieval Systems With Sparse Labels conference. Retrieved from https://aclanthology.org/2024.eacl-long.26
Sharifymoghaddam, S., Upadhyay, S., Chen, W., & Lin, J. (2024). UniRAG: Universal Retrieval Augmentation for Multi-Modal Large Language Models ArXiv, abs/2405.10311. https://doi.org/10.48550/ARXIV.2405.10311