Quantize by Drift: Label-Free Mixed-Precision Post-Training Quantization for Text Embedders
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Computer Science > Information Retrieval
arXiv:2610.09227 (cs)
[Submitted on 6 Oct 2026]
Title:Quantize by Drift: Label-Free Mixed-Precision Post-Training Quantization for Text Embedders
View a PDF of the paper titled Quantize by Drift: Label-Free Mixed-Precision Post-Training Quantization for Text Embedders, by Hyojung Han and 2 other authors
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Abstract:Mixed-precision post-training quantization needs a per-module sensitivity signal; for a text embedder the obvious one -- the retrieval quality a module costs when quantized -- needs relevance labels that deployments rarely have. We measure a label-free substitute: quantization-induced representation drift, obtained by quantizing one module, re-encoding the corpus, and recording how far the output embeddings moved from their full-precision positions. What is specific is the observable: the deployed output representation a dense retriever ranks with. Across five development embedders, configuration-level drift orders sampled mixed-precision plans against held-out retrieval quality at a macro Spearman of 0.911, the sensitivity transports across calibration corpora and retrieval domains in the usable regime, module drifts compose rank-consistently but not numerically, and relevance-derived sensitivity adds no consistent value. The method is one additive allocation under a hard packed-byte budget, with no labels and no search. On three embedders held untouched until method, baselines and hypotheses were frozen and sealed, the pre-registered directional hypothesis against the prior LieQ criterion holds (3/3 at the main budget, no collapse) and drift scores above a two-sided LieQ steelman in 2/3; but at the main budget drift is numerically lower than same-budget uniform precision on all three (-0.99, -0.85, -1.01 points), having reduced module and whole-model drift as designed. Output drift is thus a robust coarse sensitivity signal, not a universally optimal allocation objective: it avoids the catastrophic failures of the transferred signed-geometry adaptation and can remain usable at stressed budgets where uniform collapses, but fine-grained redistribution around a strong uniform operating point remains unresolved.
| Comments: | 26 pages, 22 tables, 4 figures |
| Subjects: | Information Retrieval (cs.IR); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.09227 [cs.IR] |
| (or arXiv:2610.09227v1 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.09227
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Quantize by Drift: Label-Free Mixed-Precision Post-Training Quantization for Text Embedders, by Hyojung Han and 2 other authors
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