IKS-Instruct dataset¶
Product or Programme · All products and programmes
Evidence · 2 claims (2 for) · 1 study (1 design),q2· 0 of 1 report an effect size · 2 claims rest on one study
Description¶
A released multilingual dataset of 24,795 instruction-response pairs for fine-tuning and evaluating language models on Indian Knowledge Systems pedagogy, created and released by Shwetha Singaravelu and colleagues.
Components¶
Claims¶
- IKS-Instruct fine-tuning is necessary for IKS pedagogical capability: a compact 7B fine-tune reaches a median judge score of 6.39, within 0.15 of a general-purpose reference, while the base model scores near zero on IKS-specific dimensions [+M]
- Model-level judge quality is non-monotonic in dataset size: the 13,882-pair v1.1 yields the best model (6.39), while both expansion to ~20,000 pairs (6.33) and aggressive curation to 7,130 pairs (6.12) reduce quality [+M]
Related Products and Programmes¶
- Set per-technique coverage targets rather than scaling raw volume when constructing domain-specific instruction datasets: the dataset-construction guideline drawn from its per-technique analysis
Key Sources¶
- Shwetha Singaravelu, Gayathri Muruganantham, Lakshmi Rajendran, Santhosh Sivasubramani. (2026). IKS-Instruct: A 24,000-Example Multilingual Dataset for Teaching Language Models Indian Knowledge Systems. arXiv. https://arxiv.org/abs/2607.23322
Page metadata
| Field | Value |
|---|---|
type |
product |
id |
iks-instruct-dataset |
title |
IKS-Instruct dataset |
description |
A released multilingual dataset of 24,795 instruction-response pairs for fine-tuning and evaluating language models on Indian Knowledge Systems pedagogy, created and released by Shwetha Singaravelu and colleagues. |
status |
review |
product_kind |
dataset |
generated |
by process:settle-candidates on 2026-10-11 |
trust |
unverified — no one has confirmed this page's content |
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