Arabic Mini-ClimateGPT : A Climate Change and Sustainability Tailored
  Arabic LLM
Sahal Shaji Mullappilly Abdelrahman Shaker Omkar Thawakar Hisham Cholakkal Rao Muhammad Anwer Salman Khan Fahad Shahbaz Khan
Climate change is one of the most significant challenges we face together as
a society. Creating awareness and educating policy makers the wide-ranging
impact of climate change is an essential step towards a sustainable future.
Recently, Large Language Models (LLMs) like ChatGPT and Bard have shown
impressive conversational abilities and excel in a wide variety of NLP tasks.
While these models are close-source, recently alternative open-source LLMs such
as Stanford Alpaca and Vicuna have shown promising results. However, these
open-source models are not specifically tailored for climate related domain
specific information and also struggle to generate meaningful responses in
other languages such as, Arabic. To this end, we propose a light-weight Arabic
Mini-ClimateGPT that is built on an open-source LLM and is specifically
fine-tuned on a conversational-style instruction tuning curated Arabic dataset
Clima500-Instruct with over 500k instructions about climate change and
sustainability. Further, our model also utilizes a vector embedding based
retrieval mechanism during inference. We validate our proposed model through
quantitative and qualitative evaluations on climate-related queries. Our model
surpasses the baseline LLM in 88.3% of cases during ChatGPT-based evaluation.
Furthermore, our human expert evaluation reveals an 81.6% preference for our
model's responses over multiple popular open-source models. Our open-source
demos, code-base and models are available here
https://github.com/mbzuai-oryx/ClimateGPT.