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Arabzadeh, N., Golzadeh, K., Risi, C., Clarke, C., & Zhao, J. (2024). KnowFIRES: A Knowledge-Graph Framework for Interpreting Retrieved Entities From Search Presented at the IRES: A Knowledge-Graph Framework for Interpreting Retrieved Entities From Search conference. https://doi.org/10.1007/978-3-031-56069-9_15

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

Pradeep, R., & Lin, J. (2024). Towards Automated End-to-End Health Misinformation Free Search With A Large Language Model Presented at the Automated End-to-End Health Misinformation Free Search With A Large Language Model conference. https://doi.org/10.1007/978-3-031-56066-8_9

Arabzadeh, N., Bigdeli, A., & Clarke, C. (2024). Adapting Standard Retrieval Benchmarks to Evaluate Generated Answers ArXiv, abs/2401.04842. https://doi.org/10.48550/ARXIV.2401.04842

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

He, X. (2024). Technical Perspective: Synthetic Data Needs a Reproducibility Benchmark SIGMOD Record, 53, 64. https://doi.org/10.1145/3665252.3665266

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

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

Arabzadeh, N., Kiseleva, J., Wu, Q., Wang, C., Awadallah, A., Dibia, V., … Clarke, C. (2024). Towards Better Human-Agent Alignment: Assessing Task Utility in LLM-Powered Applications ArXiv, abs/2402.09015. https://doi.org/10.48550/ARXIV.2402.09015