94 citations · 253 across the 41 of their papers we have counts for
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cs.IR2023
ReFIT: Relevance Feedback from a Reranker during Inference
Revanth Gangi Reddy, Pradeep Dasigi, Md Arafat Sultan +4
Retrieve-and-rerank is a prevalent framework in neural information retrieval, wherein a bi-encoder network initially retrieves a pre-defined number of candidates (e.g., K=100), whi…
cs.IR2023★ 1 cited
UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers
Jon Saad-Falcon, Omar Khattab, Keshav Santhanam +6
Many information retrieval tasks require large labeled datasets for fine-tuning. However, such datasets are often unavailable, and their utility for real-world applications can dim…