Tamber, M. S., Pradeep, R., & Lin, J. (2023). Pre-Processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering Presented at the Pre-Processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering conference. https://doi.org/10.1007/978-3-031-28241-6_11
References
Filter by:
Tang, R., Zhang, X., Lin, J., & Türe, F. (2023). What Do Llamas Really Think? Revealing Preference Biases in Language Model Representations ArXiv, abs/2311.18812. https://doi.org/10.48550/ARXIV.2311.18812
Yang, J.-H., Lassance, C., de Rezende, R. S., Srinivasan, K., Redi, M., Clinchant, S. ephane, & Lin, J. (2023). AToMiC: An Image/Text Retrieval Test Collection to Support Multimedia Content Creation ArXiv, abs/2304.01961. https://doi.org/10.48550/arXiv.2304.01961
Bauer, C., Carterette, B., Ferro, N., Fuhr, N., Beel, J., Breuer, T., … Zobel, J. (2023). Report on the Dagstuhl Seminar on Frontiers of Information Access Experimentation for Research and Education SIGIR Forum, 57, 1-7. https://doi.org/10.1145/3636341.3636351
Lin, J., & Teofili, T. (2023). Searching Dense Representations With Inverted Indexes ArXiv, abs/2312.01556. https://doi.org/10.48550/ARXIV.2312.01556
Pradeep, R., Chen, H., Gu, L., Tamber, M. S., & Lin, J. (2023). PyGaggle: A Gaggle of Resources for Open-Domain Question Answering Presented at the PyGaggle: A Gaggle of Resources for Open-Domain Question Answering conference. https://doi.org/10.1007/978-3-031-28241-6_10
Yang, J.-H., Lassance, C., de Rezende, R. S., Srinivasan, K., Redi, M., Clinchant, S. ephane, & Lin, J. (2023). AToMiC: An Image/Text Retrieval Test Collection to Support Multimedia Content Creation ArXiv, abs/2304.01961. https://doi.org/10.48550/arXiv.2304.01961
Qian, K., Belyi, A., Wu, F., Khorshidi, S., Nikfarjam, A., Khot, R., … Li, Y. (2023). Open Domain Knowledge Extraction for Knowledge Graphs ArXiv, abs/2312.09424. https://doi.org/10.48550/ARXIV.2312.09424
Dadvar, V., Golab, L., & Srivastava, D. (2023). POEM: Pattern-Oriented Explanations of Convolutional Neural Networks Proceedings of the VLDB Endowment (PVLDB), 16, 3192-3200. https://doi.org/10.14778/3611479.3611518
Lin, S.-C., Asai, A., Li, M., Oguz, B., Lin, J., Mehdad, Y., … Chen, X. (2023). How to Train Your Dragon: Diverse Augmentation Towards Generalizable Dense Retrieval Presented at the How to Train Your Dragon: Diverse Augmentation Towards Generalizable Dense Retrieval conference. Retrieved from https://aclanthology.org/2023.findings-emnlp.423