阿拉伯语Mini-ClimateGPT：面向气候变化与可持续发展的定制化阿拉伯语大语言模型
Arabic Mini-ClimateGPT : A Climate Change and Sustainability Tailored Arabic LLM
AI总结：
针对现有开源大语言模型缺乏气候领域定制能力、阿拉伯语生成效果不佳的问题，研究团队构建了基于开源LLM、在Clima500-Instruct数据集微调并结合向量检索的轻量级阿拉伯语Mini-ClimateGPT，在自动与人工评估中均显著优于基线模型。
摘要：
气候变化是我们作为一个社会共同面临的最重大挑战之一。提升公众认知、向政策制定者科普气候变化的广泛影响，是迈向可持续未来的关键一步。近期，ChatGPT、Bard等大语言模型（LLM）展现出出色的对话能力，在各类自然语言处理任务中表现优异。尽管这些模型是闭源的，斯坦福Alpaca、Vicuna等开源替代LLM也已取得可观成果。但这些开源模型既未针对气候领域的专属信息做定制优化，也难以生成阿拉伯语等其他语言的高质量有效回复。为此，我们提出轻量级阿拉伯语Mini-ClimateGPT，该模型基于开源LLM构建，在经整理的对话式指令调优阿拉伯语数据集Clima500-Instruct上完成专项微调，该数据集包含超50万条气候变化与可持续发展相关的指令。此外，模型在推理阶段还采用了基于向量嵌入的检索机制。我们通过气候相关查询的定量与定性评估验证了所提模型的性能：在基于ChatGPT的评估中，我们的模型在88.3%的案例中表现优于基线LLM；人类专家评估显示，相比多款主流开源模型，81.6%的情况下专家更偏好我们模型的回复。我们的开源演示、代码库与模型可访问https://github.com/mbzuai-oryx/ClimateGPT获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
