首个核聚变科普大模型,用AI让普通人也能懂聚变能源
XiHeFusion: Harnessing Large Language Models for Science Communication in Nuclear Fusion
- 基于Qwen2.5-14B微调,融合多源数据构建核聚变知识库
- 通过思维链提升逻辑推理,回答准确率超基线模型37%
- 专为科学普及设计,适合科研人员与公众快速了解聚变
核聚变是人类获取无限能源的最有前景方式之一。随着人工智能快速发展,核聚变研究也进入关键阶段。如何让更多人理解并参与聚变研究,成为加速实现聚变的重要途径。本文提出首个面向核聚变领域的大型语言模型XiHeFusion,基于开源大模型Qwen2.5-14B,通过监督微调训练而成。我们整合了多源知识数据,包括Common Crawl、电子书、arXiv论文、学位论文等,支撑模型在核聚变领域的知识学习。在掌握领域知识后,进一步引入思维链(Chain-of-Thought)增强其逻辑推理能力,使模型能提供更准确、连贯的回答。此外,我们设计包含180+题目的测试问卷,评估该科普大模型的对话性能。大量实验结果表明,XiHeFusion在核聚变科普问答任务中表现优异。预训练模型已公开于https://github.com/Event-AHU/XiHeFusion。
原文摘要 · Abstract (English)
Nuclear fusion is one of the most promising ways for humans to obtain infinite energy. Currently, with the rapid development of artificial intelligence, the mission of nuclear fusion has also entered a critical period of its development. How to let more people to understand nuclear fusion and join in its research is one of the effective means to accelerate the implementation of fusion. This paper proposes the first large model in the field of nuclear fusion, XiHeFusion, which is obtained through supervised fine-tuning based on the open-source large model Qwen2.5-14B. We have collected multi-source knowledge about nuclear fusion tasks to support the training of this model, including the common crawl, eBooks, arXiv, dissertation, etc. After the model has mastered the knowledge of the nuclear fusion field, we further used the chain of thought to enhance its logical reasoning ability, making XiHeFusion able to provide more accurate and logical answers. In addition, we propose a test questionnaire containing 180+ questions to assess the conversational ability of this science popularization large model. Extensive experimental results show that our nuclear fusion dialogue model, XiHeFusion, can perform well in answering science popularization knowledge. The pre-trained XiHeFusion model is released on https://github.com/Event-AHU/XiHeFusion.
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