常识VIS:可视化和理解自然语言模型的常识推理能力
IEEE transactions on visualization and computer graphics
|October 26, 2023
概括
大型语言模型在常识推理方面表现有希望,但它们的知识仍然不透明. CommonsenseVIS提供了一个视觉系统来探索和理解它们的隐含推理,增强NLP专家的解释能力.
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.
- 可解释的人工智能
背景情况:
- 大型预训练语言模型 (LLM) 在常识推理任务中表现出高级性能.
- 当前的可解释性方法难以揭示LLMs学到的隐性常识知识.
- 现有的技术往往无法推断模型对未陈述概念的推理.
研究的目的:
- 介绍CommonsenseVIS,这是一个用于常识问答模型的新视觉解释系统.
- 使用外部常识知识库对LLM行为进行背景化.
- 为了能够更深入地了解LLM的隐性推理能力.
主要方法:
- 从输入数据中提取相关的常识知识.
- 通过提取的引用,使模型行为与人类知识保持一致.
- 开发多层次可视化和交互式探测/编辑功能.
- 整合外部的常识知识库.
主要成果:
- 在CommonsenseVIS中,我们可以更轻松地对概念进行关系推理的分析.
- 该系统有助于理解LLM如何利用常识知识.
- 用户研究证实了该系统对NLP专家在分析模型行为的实用性.
- 证明有能力系统和可扩展地分析模型推理.
结论:
- 在常识推理任务中,CommonsenseVIS提高了LLM的解释性.
- 该系统为研究人员提供了一种有价值的工具,用于调查隐含的知识和推理模式.
- 与常识知识的可视化和互动改善了对LLM决策过程的理解.
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