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Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls., , , , and . ACM Trans. Inf. Syst., 41 (3): 62:1-62:40 (2023)Selecting which Dense Retriever to use for Zero-Shot Search., , , , and . SIGIR-AP, page 223-233. ACM, (2023)Dealing with Typos for BERT-based Passage Retrieval and Ranking., and . EMNLP (1), page 2836-2842. Association for Computational Linguistics, (2021)Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking., , , and . EMNLP (Findings), page 8807-8817. Association for Computational Linguistics, (2023)The Impact of Auxiliary Patient Data on Automated Chest X-Ray Report Generation and How to Incorporate It., , , and . CoRR, (2024)Counterfactual Online Learning to Rank., and . ECIR (1), volume 12035 of Lecture Notes in Computer Science, page 415-430. Springer, (2020)Robustness of Neural Rankers to Typos: A Comparative Study., , and . ADCS, page 6:1-6:6. ACM, (2022)TILDE: Term Independent Likelihood moDEl for Passage Re-ranking., and . SIGIR, page 1483-1492. ACM, (2021)Asyncval: A Toolkit for Asynchronously Validating Dense Retriever Checkpoints During Training., and . SIGIR, page 3235-3239. ACM, (2022)An Investigation of Prompt Variations for Zero-shot LLM-based Rankers., , , and . CoRR, (2024)