  Instruction-tuned large language models, such as T0, have demonstrated
remarkable capabilities in following instructions across various domains.
However, their proficiency remains notably deficient in many low-resource
languages. To address this challenge, we introduce FarsInstruct: a
comprehensive instruction dataset designed to enhance the instruction-following
ability of large language models specifically for the Persian language, a
significant yet underrepresented language globally. FarsInstruct encompasses a
wide range of task types and datasets, each containing a mix of straightforward
to complex manual written instructions, as well as translations from Public
Pool of Prompts, ensuring a rich linguistic and cultural representation.
Furthermore, we introduce Co-CoLA, a framework designed to enhance the
multi-task adaptability of LoRA-tuned models. Through extensive experimental
analyses, our study showcases the effectiveness of FarsInstruct dataset coupled
with training by Co-CoLA framework, in improving the performance of large
language models within the Persian context. As of the current writing,
FarsInstruct comprises more than 200 templates across 21 distinct datasets, and
we intend to update it consistently, thus augmenting its applicability.
