Enabling Structured Reasoning in Sindhi with Culturally Grounded Instruction Tuning
Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026), at EACL 2026, Rabat, Morocco
Presented on
In short
The paper introduces Sindhi-Reasoning-Instruct, a culturally grounded Sindhi instruction dataset, and fine-tunes six LLaMA and Mistral models (1B to 24B) for deductive, inductive and causal reasoning. Native, authentic data proved decisive: Mistral-Small-24B improved human quality ratings by 141% over its base version and reached 83% on inductive reasoning tasks.
Cite
@inproceedings{mehak-etal-2026-enabling,
title = "Enabling Structured Reasoning in {S}indhi with Culturally Grounded Instruction Tuning",
author = "Mehak, Mehak and
Zeinalipour, Kamyar and
Soomro, Pireh and
Chesi, Cristiano and
Gori, Marco and
Maggini, Marco",
editor = "Hettiarachchi, Hansi and
Ranasinghe, Tharindu and
Plum, Alistair and
Rayson, Paul and
Mitkov, Ruslan and
Gaber, Mohamed and
Premasiri, Damith and
Tan, Fiona Anting and
Uyangodage, Lasitha",
booktitle = "Proceedings of the Second Workshop on Language Models for Low-Resource Languages ({L}o{R}es{LM} 2026)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.loreslm-1.22/",
doi = "10.18653/v1/2026.loreslm-1.22",
pages = "239--258",
ISBN = "979-8-89176-377-7"
}