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.

翻译：气候变化是人类社会共同面临的最重大挑战之一。提高公众认知并教育政策制定者认识到气候变化的广泛影响，是实现可持续未来的关键步骤。近年来，ChatGPT和Bard等大型语言模型在对话能力及各类自然语言处理任务中展现出卓越表现。尽管这些模型为闭源系统，但近期斯坦福Alpaca和Vicuna等替代性开源大模型已取得令人瞩目的成果。然而，这些开源模型尚未针对气候领域特定信息进行专门优化，且在阿拉伯语等非英语语言中难以生成有意义的回复。为此，我们提出了轻量级阿拉伯语微型气候GPT模型，该模型基于开源大语言模型构建，并通过对话式指令调优技术，在包含超过50万条气候变化与可持续发展相关指令的阿拉伯语数据集Clima500-Instruct上进行微调。此外，我们的模型在推理阶段采用基于向量嵌入的检索机制。我们通过定量与定性评估验证了模型在气候相关查询中的表现：在基于ChatGPT的评估中，我们的模型在88.3%的案例中优于基线大语言模型；人类专家评估显示，相较于多个主流开源模型，专家组对模型回复的偏好率达81.6%。我们开源的演示程序、代码库及模型可通过https://github.com/mbzuai-oryx/ClimateGPT获取。