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IKS-Instruct: A 24,000-Example Multilingual Dataset for Teaching Language Models Indian Knowledge Systems

arXiv 2607.23322 ↗First posted 25 Jul 2026cs.CL cs.CY cs.ETcited by 0

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Abstract (arXiv)

Instruction tuning has become the standard method for adapting large language models to follow human intent, yet existing instruction datasets are dominated by English-language general-knowledge tasks and lack coverage of specialized pedagogical domains. This paper presents IKS-Instruct, a dataset of 24,795 instruction-response pairs for teaching language models to deliver educational content grounded in Indian Knowledge Systems (IKS). The dataset spans seven languages (English, Hindi, Sanskrit, Tamil, Telugu, Kannada, and Malayalam), covers 41 pedagogical techniques from the Vedic oral and mathematical traditions, and is aligned with the Central Board of Secondary Education (CBSE) curriculum for classes 6 through 12. The pairs are derived from six source types: classical text corpora (Bhagavad Gita, Thirukkural, Sangam literature, Vedic texts), curriculum-aligned pedagogical templates, Vedic mathematical sutra demonstrations, bilingual instruction pairs, technique-grounded multi-turn dialogues, and cross-tradition comparative analyses. Quality is assessed through a multi-judge evaluation framework in which independent language models score responses on 12 dimensions including technique fidelity, pedagogical quality, factual accuracy, and IKS cultural depth. Under a uniform five-judge external panel (median aggregation over 1,201 stratified items), the strongest IKS-Instruct fine-tune of a compact 7B model reaches a median judge score of 6.39, within 0.15 of a strong general-purpose reference model (Nemotron-Nano at 6.54) at a fraction of its deployment cost, while the base model without IKS fine-tuning scores near zero on the IKS-specific dimensions. Model quality does not increase monotonically with data curation, a result we report together with the corresponding data-quality gains.

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References · 15

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  3. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  4. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  5. 2022Large Language Models Encode Clinical Knowledgecited by 1,807
  6. 2023BloombergGPT: A Large Language Model for Financecited by 923
  7. 2023MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problemscited by 128
  8. 2023Bactrian-X: Multilingual Replicable Instruction-Following Models with Low-Rank Adaptationcited by 79
  9. 2024mCoT: Multilingual Instruction Tuning for Reasoning Consistency in Language Modelscited by 31
  10. 2023The BEA 2023 Shared Task on Generating AI Teacher Responses in Educational Dialoguescited by 27
  11. 2025Massive Supervised Fine-tuning Experiments Reveal How Data, Layer, and Training Factors Shape LLM Alignment Qualitycited by 14
  12. 2024Zero-shot cross-lingual transfer in instruction tuning of large language modelscited by 10
3 more
  1. 2023IndicIRSuite: Multilingual Dataset and Neural Information Models for Indian Languagescited by 5
  2. 2026Indic-TunedLens: Interpreting Multilingual Models in Indian Languagescited by 2
  3. 2026NE-BERT: A Multilingual Language Model for Nine Northeast Indian Languagescited by 1

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