Lin, J., & Teofili, T. (2023). Searching Dense Representations With Inverted Indexes ArXiv, abs/2312.01556. https://doi.org/10.48550/ARXIV.2312.01556
References
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2023
Li, M., Lin, S.-C., Ma, X., & Lin, J. (2023). SLIM: Sparsified Late Interaction for Multi-Vector Retrieval With Inverted Indexes ArXiv, abs/2302.06587. https://doi.org/10.48550/arXiv.2302.06587
Clarke, C., Diaz, F., & Arabzadeh, N. (2023). Preference-Based Offline Evaluation Presented at the Preference-Based Offline Evaluation conference. https://doi.org/10.1145/3539597.3572725
Tang, R., Zhang, X., Ma, X., Lin, J., & Türe, F. (2023). Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language Models ArXiv, abs/2310.07712. https://doi.org/10.48550/ARXIV.2310.07712
Ilyas, I., Lacerda, J. P., Li, Y., Minhas, U. F., Mousavi, A., Pound, J., … Sumanth, C. (2023). Growing and Serving Large Open-Domain Knowledge Graphs ArXiv, abs/2305.09464. https://doi.org/10.48550/arXiv.2305.09464
Pradeep, R., Hui, K., Gupta, J., Lelkes, A. am D., Zhuang, H., Lin, J., … Tran, V. Q. (2023). How Does Generative Retrieval Scale to Millions of Passages? Presented at the How Does Generative Retrieval Scale to Millions of Passages? conference. Retrieved from https://aclanthology.org/2023.emnlp-main.83
Tamber, M. S., Pradeep, R., & Lin, J. (2023). Scaling Down, LiTting Up: Efficient Zero-Shot Listwise Reranking With Seq2seq Encoder-Decoder Models ArXiv, abs/2312.16098. https://doi.org/10.48550/ARXIV.2312.16098
Buchanan, G. R., McKay, D., & Clarke, C. (2023). Made to Measure: A Workshop on Human-Centred Metrics for Information Seeking Presented at the Made to Measure: A Workshop on Human-Centred Metrics for Information Seeking conference. https://doi.org/10.1145/3576840.3578301
Huang, C., Xie, Y., Jiang, Z., Lin, J., & Li, M. (2023). Approximating Human-Like Few-Shot Learning With GPT-based Compression ArXiv, abs/2308.06942. https://doi.org/10.48550/arXiv.2308.06942
O\textquoterightHalloran, T., McManus, B., Harbison, A., Grossman, M., & Cormack, G. (2023). Technology-Assisted Review for Spreadsheets and Noisy Text Presented at the Technology-Assisted Review for Spreadsheets and Noisy Text conference. https://doi.org/10.1145/3573128.3609341