Accelerating String-Heavy Queries with LLM Token Tables
Summary: Repurposes GPT-4’s tokenizer as a globally shared string token table, letting joins and aggregations operate directly on encoded values. Umbra achieves up to 2× faster string-heavy queries, 1.65× lower storage/memory, and >6 GB/s decoding. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Tobias Schmidt (Technical University of Munich)
- 2. Nicolas Schmitt (Technical University of Munich)
- 3. Thomas Neumann (Technical University of Munich)
- 4. Andreas Kipf (University of Technology Nuremberg)
BibTeX Citation
@article{schmidt_vldb26,
title = {{Accelerating String-Heavy Queries with LLM Token Tables}},
author = {Schmidt, Tobias and Schmitt, Nicolas and Neumann, Thomas and Kipf, Andreas},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {11},
pages = {3635--3648},
doi = {10.14778/3836663.3836714},
url = {https://doi.org/10.14778/3836663.3836714},
year = {2026}
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