The ROOTS Search Tool: Data Transparency for LLMs
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Abstract (arXiv)
ROOTS is a 1.6TB multilingual text corpus developed for the training of BLOOM, currently the largest language model explicitly accompanied by commensurate data governance efforts. In continuation of these efforts, we present the ROOTS Search Tool: a search engine over the entire ROOTS corpus offering both fuzzy and exact search capabilities. ROOTS is the largest corpus to date that can be investigated this way. The ROOTS Search Tool is open-sourced and available on Hugging Face Spaces. We describe our implementation and the possible use cases of our tool.
Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). Builds on 35 of its 59 references that are in the map. First posted on arXiv 27 Feb 2023.
Builds on
35 of 59 references.
References · 35
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23 more
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- 2016Bag of Tricks for Efficient Text Classificationcited by 869
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- 2022Deduplicating Training Data Mitigates Privacy Risks in Language Modelscited by 309
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Citing papers per year: 2023: 12; 2024: 10; 2025: 5; 2026: 3.
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- 2025Getting Your Indices in a Row: Full-Text Search for LLM Training Data for Real World10 Oct 2025
- 2025What Is The Political Content in LLMs' Pre- and Post-Training Data?26 Sep 2025
- 2025Data Leakage in Visual Datasets24 Aug 2025
- 2025A Conceptual Framework for AI Capability Evaluations23 Jun 2025
- 2025Beyond Release: Access Considerations for Generative AI Systems23 Feb 2025
- 2024Private Synthetic Text Generation with Diffusion Models30 Oct 2024
- 2024Does Data Contamination Detection Work (Well) for LLMs? A Survey and Evaluation on Detection Assumptions24 Oct 2024
- 2024What's New in My Data? Novelty Exploration via Contrastive Generation18 Oct 2024
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18 more
- 2024The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources24 Jun 2024
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- 2024Towards Transparency: Exploring LLM Trainings Datasets through Visual Topic Modeling and Semantic Frame3 Jun 2024
- 2024Dated Data: Tracing Knowledge Cutoffs in Large Language Models19 Mar 2024
- 2024On Catastrophic Inheritance of Large Foundation Models2 Feb 2024
- 2024Infini-gram: Scaling Unbounded n-gram Language Models to a Trillion Tokens30 Jan 2024
- 2023Oasis: Data Curation and Assessment System for Pretraining of Large Language Models21 Nov 2023
- 2023Investigating Data Contamination in Modern Benchmarks for Large Language Models16 Nov 2023
- 2023NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark27 Oct 2023
- 2023Cultural Adaptation of Recipes26 Oct 2023
- 2023Establishing Trustworthiness: Rethinking Tasks and Model Evaluation9 Oct 2023
- 2023Position: Key Claims in LLM Research Have a Long Tail of Footnotes14 Aug 2023
- 2023OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents21 Jun 2023
- 2023Evaluating the Social Impact of Generative AI Systems in Systems and Society9 Jun 2023
- 2023Stop Uploading Test Data in Plain Text: Practical Strategies for Mitigating Data Contamination by Evaluation Benchmarks17 May 2023
- 2023StarCoder: may the source be with you!9 May 2023
- 2023Multi-step Jailbreaking Privacy Attacks on ChatGPT11 Apr 2023
- 2023Data Portraits: Recording Foundation Model Training Data6 Mar 2023
- 2024Private Synthetic Text Generation with Diffusion Modelscited by 10
- 2024Private Synthetic Text Generation with Diffusion Models30 Oct 2024
- 2023StarCoder: may the source be with you!cited by 790
- 2023NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmarkcited by 339
- 2024Infini-gram: Scaling Unbounded n-gram Language Models to a Trillion Tokenscited by 148
- 2023Stop Uploading Test Data in Plain Text: Practical Strategies for Mitigating Data Contamination by Evaluation Benchmarkscited by 139
- 2023Investigating Data Contamination in Modern Benchmarks for Large Language Modelscited by 136
- 2023Evaluating the Social Impact of Generative AI Systems in Systems and Societycited by 91
- 2024Dated Data: Tracing Knowledge Cutoffs in Large Language Modelscited by 48
- 2024Unveiling the Spectrum of Data Contamination in Language Models: A Survey from Detection to Remediationcited by 30
- 2023Data Portraits: Recording Foundation Model Training Datacited by 29
- 2024Does Data Contamination Detection Work (Well) for LLMs? A Survey and Evaluation on Detection Assumptionscited by 26
- 2023Position: Key Claims in LLM Research Have a Long Tail of Footnotescited by 20
- 2024On Catastrophic Inheritance of Large Foundation Modelscited by 15
12 most cited of 25 All 25 (JSON)
- 2026Auditing Chinese Web-scale Corpora via Sampled BPE Token Statistics11 Aug 2026
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- 2026Personal Information Parroting in Language Models24 Feb 2026
- 2025What Is The Political Content in LLMs' Pre- and Post-Training Data?26 Sep 2025
- 2025Data Leakage in Visual Datasets24 Aug 2025
- 2025A Conceptual Framework for AI Capability Evaluations23 Jun 2025
- 2025Beyond Release: Access Considerations for Generative AI Systems23 Feb 2025
- 2024Does Data Contamination Detection Work (Well) for LLMs? A Survey and Evaluation on Detection Assumptions24 Oct 2024
- 2024What's New in My Data? Novelty Exploration via Contrastive Generation18 Oct 2024
- 2024A Taxonomy for Data Contamination in Large Language Models11 Jul 2024
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12 newest of 25 All 25 (JSON)