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.

我们提出了一个基于开源大型语言模型的轻量级阿拉伯迷你气候GPT，该模型经过细调，并利用矢量嵌入检索机制在推理过程中。我们通过定量和定性评估验证了我们提出的模型在与气候相关的查询中的优越性，并通过人工专家评估显示出对我们模型响应的81.6%偏好。

阿拉伯迷你气候GPT：定制的阿拉伯气候变化与可持续性文本生成模型