146 citations · 408 across the 42 of their papers we have counts for
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cs.IR2022★ 14 cited
P^3 Ranker: Mitigating the Gaps between Pre-training and Ranking Fine-tuning with Prompt-based Learning and Pre-finetuning
Xiaomeng Hu, Shi Yu, Chenyan Xiong +3
Compared to other language tasks, applying pre-trained language models (PLMs) for search ranking often requires more nuances and training signals. In this paper, we identify and st…
cs.IR2022★ 1 cited
Dimension Reduction for Efficient Dense Retrieval via Conditional Autoencoder
Zhenghao Liu, Han Zhang, Chenyan Xiong +3
Dense retrievers encode queries and documents and map them in an embedding space using pre-trained language models. These embeddings need to be high-dimensional to fit training sig…