ClimateChat：为指令微调大语言模型以回答气候变化查询设计数据与方法
ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries
AI总结：
本研究针对气候变化领域高精度指令数据不足的问题，提出自动化构建指令数据的方法，构建ClimateChat-Corpus数据集并微调得到ClimateChat大语言模型，显著提升了气候变化问答任务性能，为领域大模型训练提供参考。
摘要：
随着全球气候变化问题日益严峻，气候科学领域的研究需求持续增长。以大语言模型（Large Language Models, LLMs）为代表的自然语言处理技术已被广泛应用于气候变化专项研究，为决策者和公众提供必要的信息支持。已有研究通过构建气候变化相关指令数据并对大语言模型进行指令微调，提升了模型在相关任务上的表现。但当前研究在高效生成大量高精度气候变化指令数据方面仍存在不足，限制了气候变化大语言模型的进一步发展。本研究提出一种自动化构建指令数据的方法，该方法利用文档中的事实与背景知识生成指令，并通过网页爬取和种子指令收集增强指令数据的多样性。我们采用该方法构建了一个名为ClimateChat-Corpus的气候变化指令数据集，并用其微调开源大语言模型，得到了名为ClimateChat的大语言模型。评估结果显示，ClimateChat在气候变化问答任务上的性能得到显著提升。此外，我们评估了不同基础模型和指令数据对大语言模型性能的影响，证明了其能够适配广泛的气候变化科学发现任务，同时强调了为指令微调选择合适基础模型的重要性。本研究为气候变化指令数据构建及气候变化专项大语言模型训练提供了有价值的参考与实证支持。
英文摘要：
As the issue of global climate change becomes increasingly severe, the demand for research in climate science continues to grow. Natural language processing technologies, represented by Large Language Models (LLMs), have been widely applied to climate change-specific research, providing essential information support for decision-makers and the public. Some studies have improved model performance on relevant tasks by constructing climate change-related instruction data and instruction-tuning LLMs. However, current research remains inadequate in efficiently producing large volumes of high-precision instruction data for climate change, which limits further development of climate change LLMs. This study introduces an automated method for constructing instruction data. The method generates instructions using facts and background knowledge from documents and enhances the diversity of the instruction data through web scraping and the collection of seed instructions. Using this method, we constructed a climate change instruction dataset, named ClimateChat-Corpus, which was used to fine-tune open-source LLMs, resulting in an LLM named ClimateChat. Evaluation results show that ClimateChat significantly improves performance on climate change question-and-answer tasks. Additionally, we evaluated the impact of different base models and instruction data on LLM performance and demonstrated its capability to adapt to a wide range of climate change scientific discovery tasks, emphasizing the importance of selecting an appropriate base model for instruction tuning. This research provides valuable references and empirical support for constructing climate change instruction data and training climate change-specific LLMs.
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。
