LightRetriever reports query encoding 1,000 times faster and 10 times higher throughput while retaining 95% of benchmark performance.
LightRetriever proposes keeping document processing with language models offline and using a simpler query encoder during inference. The approach aims to reduce online costs for LLM-based text retrieval.
According to the report, query encoding was 1,000 times faster and throughput increased 10 times, while retaining 95% of benchmark performance. These are reported results, not a guarantee for other datasets or systems.
To evaluate the technique, compare it with your current setup using representative queries and documents. Measure latency, throughput, retrieval quality, and costs under realistic workloads.
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