HypoChainer:一个协作系统,结合LLMs和知识图表,以进行假设驱动的科学发现
IEEE transactions on visualization and computer graphics
|December 3, 2025
概括
通过整合人类专业知识,大型语言模型 (LLM) 和知识图 (KG),HypoChainer增强了生物发现. 这个框架可视地过深度学习预测,使得有效的假设生成和实验验证成为可能.
科学领域:
- * 计算生物学 计算生物学
- * 科学中的人工智能
- *知识的发现知识的发现.
背景情况:
- * 现代科学发现面临着巨大的数据和人类认知的局限性带来的挑战.
- *像图形神经网络 (GNN) 这样的深度学习模型产生了大量的预测,压倒了手动验证.
- * 大型语言模型 (LLM) 是有前途的,但与幻觉和缺乏结构化的知识基础等可靠性问题作斗争.
研究的目的:
- * 推出HypoChainer,一个协作可视化框架,用于增强科学发现.
- * 解决GNN预测的手动过和不可靠的LLM输出的局限性.
- * 整合人类的专业知识,法学士推理和知识图 (KG) 用于视觉假设生成.
主要方法:
- * 提取增强的LLM (RAG) 和可视化,用于对GNN预测进行上下文探索.
- *使用KG探索和LLM引导的改进进行代假设构建.
- *视觉分析用于基于精细的假设链和KG证据进行验证选择的优先预测.
主要成果:
- *HypoChainer促进了GNN预测的高效过,用于实验验证.
- * 该框架支持领域专家在产生和完善新的科学假设.
- *视觉探索和LLM驱动的推理提高了科学调查的可靠性和重点.
结论:
- *HypoChainer提供了一种新的方法来克服大规模科学发现的挑战.
- *将人类专业知识与LLM和KG等AI工具相结合,对于推进研究至关重要.
- *视觉框架提高了假设生成和验证选择的效率和有效性.
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