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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)Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls., , , , and . CoRR, (2021)ReSLLM: Large Language Models are Strong Resource Selectors for Federated Search., , , and . CoRR, (2024)Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation., , , , , , and . CoRR, (2022)Fast Passage Re-ranking with Contextualized Exact Term Matching and Efficient Passage Expansion., and . CoRR, (2021)Team IELAB at TREC Clinical Trial Track 2023: Enhancing Clinical Trial Retrieval with Neural Rankers and Large Language Models., , and . CoRR, (2024)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)A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models., , , and . CoRR, (2023)