@inproceedings{su-etal-2026-tablecache,
title = "{T}able{C}ache: Primary Foreign Key Guided {KV} Cache Precomputation for Low Latency Text-to-{SQL}",
author = "Su, Jinbo and
Hu, Yuxuan and
Li, Cuiping and
Chen, Hong and
Li, Jia and
Ma, Lintao and
Zhang, Jing",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.459/",
pages = "10551--10563",
ISBN = "979-8-89176-459-0",
abstract = "In Text-to-SQL tasks, existing LLM-based methods often include extensive database schemas in prompts, leading to long context lengths and increased prefilling latency. While user queries typically focus on recurrent table sets{---}offering an opportunity for KV cache sharing across queries{---}current inference engines, such as SGLang and vLLM, generate redundant prefix cache copies when processing user queries with varying table orders. To address this inefficiency, we propose precomputing table representations as KV caches offline and querying the required ones online. A key aspect of our approach is the computation of table caches while preserving primary foreign key relationships between tables. Additionally, we construct a Table Trie structure to facilitate efficient KV cache lookups during inference. To enhance cache performance, we introduce a cache management module with a query-reranking strategy to improve cache hit rates and a computation-loading pipeline for parallelizing model inference and cache loading. Experimental results show that our proposed TableCache achieves up to a 3.62{\texttimes} speedup in Time to First Token (TTFT) with negligible performance degradation."
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<abstract>In Text-to-SQL tasks, existing LLM-based methods often include extensive database schemas in prompts, leading to long context lengths and increased prefilling latency. While user queries typically focus on recurrent table sets—offering an opportunity for KV cache sharing across queries—current inference engines, such as SGLang and vLLM, generate redundant prefix cache copies when processing user queries with varying table orders. To address this inefficiency, we propose precomputing table representations as KV caches offline and querying the required ones online. A key aspect of our approach is the computation of table caches while preserving primary foreign key relationships between tables. Additionally, we construct a Table Trie structure to facilitate efficient KV cache lookups during inference. To enhance cache performance, we introduce a cache management module with a query-reranking strategy to improve cache hit rates and a computation-loading pipeline for parallelizing model inference and cache loading. Experimental results show that our proposed TableCache achieves up to a 3.62× speedup in Time to First Token (TTFT) with negligible performance degradation.</abstract>
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%0 Conference Proceedings
%T TableCache: Primary Foreign Key Guided KV Cache Precomputation for Low Latency Text-to-SQL
%A Su, Jinbo
%A Hu, Yuxuan
%A Li, Cuiping
%A Chen, Hong
%A Li, Jia
%A Ma, Lintao
%A Zhang, Jing
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F su-etal-2026-tablecache
%X In Text-to-SQL tasks, existing LLM-based methods often include extensive database schemas in prompts, leading to long context lengths and increased prefilling latency. While user queries typically focus on recurrent table sets—offering an opportunity for KV cache sharing across queries—current inference engines, such as SGLang and vLLM, generate redundant prefix cache copies when processing user queries with varying table orders. To address this inefficiency, we propose precomputing table representations as KV caches offline and querying the required ones online. A key aspect of our approach is the computation of table caches while preserving primary foreign key relationships between tables. Additionally, we construct a Table Trie structure to facilitate efficient KV cache lookups during inference. To enhance cache performance, we introduce a cache management module with a query-reranking strategy to improve cache hit rates and a computation-loading pipeline for parallelizing model inference and cache loading. Experimental results show that our proposed TableCache achieves up to a 3.62× speedup in Time to First Token (TTFT) with negligible performance degradation.
%U https://aclanthology.org/2026.emnlp-main.459/
%P 10551-10563
Markdown (Informal)
[TableCache: Primary Foreign Key Guided KV Cache Precomputation for Low Latency Text-to-SQL](https://aclanthology.org/2026.emnlp-main.459/) (Su et al., EMNLP 2026)
ACL