ConceptViz:一种用于探索大型语言模型中的概念的视觉分析方法.
IEEE transactions on visualization and computer graphics
|November 24, 2025
概括
ConceptViz是一个新的视觉分析系统,可以帮助研究人员理解大型语言模型 (LLM) 中的概念. 它使得解释LLM特征变得更加有效和准确.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
- 数据可视化 数据可视化
背景情况:
- 大型语言模型 (LLM) 展示了先进的能力,但它们的内部知识表示仍然不透明.
- 稀疏自编码器 (SAEs) 提供了一种从LLM中提取特征的方法,但它们的输出缺乏人类直接的解释性.
- 弥合SAE特征和人类可以理解的概念之间的差距对于提高LLM解释性至关重要.
研究的目的:
- 介绍ConceptViz,一个视觉分析系统,旨在促进LLMs内的概念的探索和解释.
- 使研究人员能够使用人类概念查询SAE特征并验证已识别的对应.
主要方法:
- ConceptViz采用了一个新的识别 ⇒ 解释 ⇒ 验证管道.
- 用户可以查询特定概念的SAE,交互探索特征对齐,并通过模型行为验证这些对齐.
主要成果:
- ConceptViz简化了在LLMs中发现和验证有意义的概念表示.
- 该系统帮助研究人员开发更准确的LLM特征的心理模型.
- 通过使用场景和用户研究证明了有效性.
结论:
- ConceptViz显著提高了LLMs的解释性研究过程.
- 该系统有助于更深入地了解LLM如何表示知识.
- 公共可用的代码和用户指南促进进一步的研究和采用.
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