知识VIS:通过比较填空提示来解释语言模型
IEEE transactions on visualization and computer graphics
|December 25, 2023
概括
KnowledgeVIS是一个视觉分析系统,通过分析它们的文本预测,帮助研究人员理解大型语言模型 (LLM). 它揭示了学习的关联和模式,有助于模型的解释和评估.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 越来越多地用于各种基于文本的任务.
- 对于研究人员和工程师来说,了解LLM的内部运作和学术关联是至关重要的.
- 现有的解释方法可能无法完全捕捉到LLM行为的细微差别.
研究的目的:
- 为了介绍KnowledgeVIS,一个新的人在循环视觉分析系统.
- 通过填空句子提示,实现语言模型的直观解释.
- 为了促进学习协会和模型预测的分析.
主要方法:
- 开发了知识VIS,这是一个用于LLM解释的视觉分析系统.
- 使用填空句子作为提示引出模型预测.
- 实施了一种新的语义聚类技术来分析预测的单词.
- 设计交互式可视化来探索预测的可能性,独特性和关系.
主要成果:
- 知识VIS有效地揭示了培训数据和下游NLP任务之间的学习关联.
- 用户可以创建和测试提示变化,分析预测,并发现见解.
- 可视化有助于识别各种提示中的预测概率,独特性和模式.
- 通过专家反和涉及生物医学知识,刻板印象和事实发现的用例来证明能力.
结论:
- KnowledgeVIS提供了一种直观而有效的方法来解释大型语言模型.
- 该系统有助于理解LLM如何学习和应用知识.
- 它支持对LLM进行偏见,知识和事实准确性的评估.
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