---
title: "Who cites IndicLLMSuite: A Blueprint for — 31 citing papers"
description: "31 papers of the Elamur Research library cite «IndicLLMSuite: A Blueprint for Creating Pre-training and Fine-Tuning Datasets for Indian…"
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# Who cites «IndicLLMSuite: A Blueprint for Creating Pre-training and Fine-Tuning Datasets for Indian Languages»

31 papers of our library cite it (of the 149,439 whose bibliographies we read) · 9 with a page here, the most cited first · [← back to «IndicLLMSuite: A Blueprint for Creating Pre-training and Fine-Tuning Datasets for Indian Languages»](https://md.research.elamur.ai/free/en/papers/khan-2024-indicllmsuite-blueprint-creating-pre-training/)

1. [**FineWeb2: One Pipeline to Scale Them All — Adapting Pre-Training Data Processing to Every Language arXiv:2506.20920**Guilherme Penedo, Hynek Kydliček, Vinko Sabolčec, et al.*2025 · 99 citations*](https://md.research.elamur.ai/free/en/papers/penedo-2025-fineweb2-one-pipeline-scale-them/)
2. **[Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translation](https://md.research.elamur.ai/free/en/papers/iyer-2024-quality-quantity-data-scale-diversity/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Vivek Iyer; Bhavitvya Malik; Pavel Stepachev; Pinzhen Chen; Barry Haddow;, Alexandra Birch*2024 · 17 citations*
3. [**Better to Ask in English: Evaluation of Large Language Models on English, Low-resource and Cross-Lingual Settings arXiv:2410.13153**Krishno Dey, Prerona Tarannum, Md. Arid Hasan, et al.*2024 · 16 citations*](https://md.research.elamur.ai/free/en/papers/dey-2024-better-ask-english-evaluation-large/)
4. **[Paramanu: Compact and Competitive Monolingual Language Models for Low-Resource Morphologically Rich Indian Languages](https://md.research.elamur.ai/free/en/papers/niyogi-2026-paramanu-compact-competitive-monolingual-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mitodru Niyogi, Eric Gaussier, Arnab Bhattacharya*2026 · 5 citations*
5. **[Layer-wise Swapping for Generalizable Multilingual Safety](https://md.research.elamur.ai/free/en/papers/shin-2026-layer-wise-swapping-generalizable-multilingual/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Hyunseo Shin, Wonseok Hwang*2026 · 1 citation*
6. **[Benchmark Data Contamination in Underrepresented Languages: A Comprehensive Analysis Using Brazilian Data](https://md.research.elamur.ai/free/en/papers/vilar-2026-benchmark-data-contamination-underrepresented-languages/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Iriedson Souto Maior de Moraes Vilar, David Candeia Maia, João Brunet, et al.*2026*
7. **[IndicSteer: Inference-Time Safety Steering for Indic LLMs](https://md.research.elamur.ai/free/en/papers/muhammad-2026-indicsteer-inference-time-safety-steering/) [Workshop on Stereotypes Across Cultures in Language Technologies](https://md.research.elamur.ai/free/en/venues/stereacult/)**Ruhaib Muhammad, Saahas Vijayalakshmi Rajaram, Suriya Priyan Durairaj*2026*
8. **[MUTANT: A Recipe for Multilingual Tokenizer Design](https://md.research.elamur.ai/free/en/papers/rana-2026-mutant-recipe-multilingual-tokenizer-design/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Souvik Rana, Ashish Kulkarni, Arul Menezes, et al.*2026*
9. **[Universal Dependencies for Sindhi](https://md.research.elamur.ai/free/en/papers/bauer-2025-universal-dependencies-sindhi/) [Universal Dependencies Workshop (UDW)](https://md.research.elamur.ai/free/en/venues/udw/)**John Bauer, Sakiina Shah, Muhammad Shaheer, et al.*2025*

22 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/khan-2024-indicllmsuite-blueprint-creating-pre-training/) — IndicLLMSuite: A Blueprint for Creating Pre-training and Fine-Tuning Datasets for Indian Languages