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Shallow Cross-Encoders for Low-Latency Retrieval
April 1, 2024, 4:47 a.m. | Aleksandr V. Petrov, Sean MacAvaney, Craig Macdonald
cs.CL updates on arXiv.org arxiv.org
Abstract: Transformer-based Cross-Encoders achieve state-of-the-art effectiveness in text retrieval. However, Cross-Encoders based on large transformer models (such as BERT or T5) are computationally expensive and allow for scoring only a small number of documents within a reasonably small latency window. However, keeping search latencies low is important for user satisfaction and energy usage. In this paper, we show that weaker shallow transformer models (i.e., transformers with a limited number of layers) actually perform better than full-scale …
abstract art arxiv bert cs.cl cs.ir documents however latency low retrieval scoring search small state text transformer transformer models type
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