mlsanityData to 7 Oct 2026

XiHeFusion: Harnessing Large Language Models for Science Communication in Nuclear Fusion

arXiv 2502.05615 ↗First posted 8 Feb 2025cs.CV cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 33 of its 53 references that are in the map. First posted on arXiv 8 Feb 2025.

Builds on

33 of 53 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

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  1. 2025Automated Construction of a Knowledge Graph of Nuclear Fusion Energy for Effective Elicitation and Retrieval of Information
  2. 2025Unlocking the Potential of Large Language Models in the Nuclear Industry with Synthetic Data
  3. 2024A recent evaluation on the performance of LLMs on radiation oncology physics using questions of randomly shuffled options
  4. 2025Large Language Models: New Opportunities for Access to Science ↗
  5. 2024Evaluating ChatGPT on Nuclear Domain-Specific Data
  6. 2025Multi-Agent Design Assistant for the Simulation of Inertial Fusion Energy
  7. 2026A Multi-Agent Human-LLM Collaborative Framework for Closed-Loop Scientific Literature Summarization
  8. 2025Advancing AI-Scientist Understanding: Multi-Agent LLMs with Interpretable Physics Reasoning