在大型语言模型中对文本总结进行数据集规模和特征导向评估,这促使数据集规模和特征导向评估
IEEE transactions on visualization and computer graphics
|September 9, 2024
概括
本研究介绍了一种以特征为导向的工作流程和用于评估大型语言模型 (LLM) 提示的Awesum系统,简化了对文本总结和其他NLG任务的复杂提示评估.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 人与计算机的交互
背景情况:
- 大型语言模型 (LLM) 和快速工程的进步使聊天机器人定制变得民主化.
- 由于评估众多测试实例的复杂性,在规模上快速评估仍然具有挑战性.
- 现有的方法通常依赖于传统的质量指标,这可能不足以进行细微的及时评估.
研究的目的:
- 为解决大型语言模型 (LLM) 快速评估的复杂性.
- 引入以特征为导向的工作流程,以便系统地及时评估.
- 介绍Awesum,一个视觉分析系统,旨在帮助快速改进文本总结.
主要方法:
- 进行了全面的文献审查和试点研究,以确定快速评估中的挑战.
- 开发了一个以特征为导向的工作流程,使用总结特征 (例如,复杂性,形式性) 而不是像ROUGE这样的传统指标.
- 介绍了Awesum,这是一个视觉分析系统,具有新的Prompt比较器,用于交互式提示提炼.
主要成果:
- 以特征为导向的工作流和Awesum系统简化了非技术用户的系统快速评估.
- 该系统有效地帮助识别文本总结的最佳快速改进.
- 实践者评估证实了该系统在各个领域的有效性和普遍适用性.
结论:
- 提出的以特征为导向的工作流程和Awesum系统显著降低了系统及时评估的障碍.
- 该方法显示了对其他自然语言生成 (NLG) 和图像生成任务的概括潜力.
- 未来的研究应该专注于以特征为导向的评估和在LLM提示工程中的人与代理互动的挑战.
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