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    <abstract>Language models are trained mainly on massive text data from the Internet, and it becomes increasingly important to understand this data source. Exact-match search engines enable searching in large text corpora – counting string appearances and retrieving the enclosing documents – yet the high storage overhead hinders their application on Internet-scale data. We present Infini-gram mini, an efficient and scalable system that can make petabyte-level text corpora searchable. Based on the FM-index data structure (Ferragina and Manzini, 2000), which simultaneously indexes and compresses text, our system creates indexes with size only 44% of the corpus. Infini-gram mini greatly improves upon the best existing implementation of FM-index in terms of indexing speed (18\times) and memory use during both indexing (3.2\times reduction) and querying (down to a negligible amount). We index 83TB of Internet text in 99 days with a single 128-core CPU node (or 19 hours if using 137 such nodes). We show one important use case of Infini-gram mini in a large-scale analysis of benchmark contamination. We find several core LM evaluation benchmarks to be heavily contaminated in Internet crawls (up to 74.2% in GSM8K), which could lead to overestimating the capabilities of language models if trained on such data. We host a benchmark contamination bulletin to share the contamination rate of many core and community-contributed benchmarks. We also release a web interface and an API endpoint to serve general search queries on Infini-gram mini indexes.</abstract>
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%0 Conference Proceedings
%T Infini-gram mini: Exact n-gram Search at the Internet Scale with FM-Index
%A Xu, Hao
%A Liu, Jiacheng
%A Choi, Yejin
%A Smith, Noah A.
%A Hajishirzi, Hannaneh
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F xu-etal-2025-infini
%X Language models are trained mainly on massive text data from the Internet, and it becomes increasingly important to understand this data source. Exact-match search engines enable searching in large text corpora – counting string appearances and retrieving the enclosing documents – yet the high storage overhead hinders their application on Internet-scale data. We present Infini-gram mini, an efficient and scalable system that can make petabyte-level text corpora searchable. Based on the FM-index data structure (Ferragina and Manzini, 2000), which simultaneously indexes and compresses text, our system creates indexes with size only 44% of the corpus. Infini-gram mini greatly improves upon the best existing implementation of FM-index in terms of indexing speed (18\times) and memory use during both indexing (3.2\times reduction) and querying (down to a negligible amount). We index 83TB of Internet text in 99 days with a single 128-core CPU node (or 19 hours if using 137 such nodes). We show one important use case of Infini-gram mini in a large-scale analysis of benchmark contamination. We find several core LM evaluation benchmarks to be heavily contaminated in Internet crawls (up to 74.2% in GSM8K), which could lead to overestimating the capabilities of language models if trained on such data. We host a benchmark contamination bulletin to share the contamination rate of many core and community-contributed benchmarks. We also release a web interface and an API endpoint to serve general search queries on Infini-gram mini indexes.
%U https://aclanthology.org/2025.emnlp-main.1268/
%P 24955-24980

Markdown (Informal)

[Infini-gram mini: Exact n-gram Search at the Internet Scale with FM-Index](https://aclanthology.org/2025.emnlp-main.1268/) (Xu et al., EMNLP 2025)

• Infini-gram mini: Exact n-gram Search at the Internet Scale with FM-Index (Xu et al., EMNLP 2025)

ACL

• Hao Xu, Jiacheng Liu, Yejin Choi, Noah A. Smith, and Hannaneh Hajishirzi. 2025. Infini-gram mini: Exact n-gram Search at the Internet Scale with FM-Index. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 24955–24980, Suzhou, China. Association for Computational Linguistics.