可视化大模型词嵌入时，用多组件展示降维不确定性。
LayerFlow: Layer-wise Exploration of LLM Embeddings using Uncertainty-aware Interlinked Projections
- 通过互连投影设计，动态呈现嵌入降维过程
- 用凸包、距离、聚类摘要等量化显示数据失真风险
- 适合研究模型可解释性或文本相似性的研究人员
大型语言模型（LLMs）通过上下文词嵌入编码语义、语法等多种语言特性。理解这些特性对研究模型能力、文本相似性任务及归因方法中重要性分析至关重要。嵌入探索常采用降维技术，将高维向量映射到二维坐标用于散点图展示。该转换过程引入不确定性，可能影响用户对数据的解读。为此，我们提出LayerFlow——一个可视化分析工作台，通过互连投影设计展示嵌入，并传递转换、表示与解释层面的不确定性。工作台包含凸包（显示2D与高维聚类）、数据点成对距离、聚类摘要及投影质量指标等视觉组件，提示潜在数据扭曲和不确定性。通过复现与专家案例研究，验证了多视觉组件和多视角对传达不确定性的重要性。
原文摘要 · Abstract (English)
Large language models (LLMs) represent words through contextual word embeddings encoding different language properties like semantics and syntax. Understanding these properties is crucial, especially for researchers investigating language model capabilities, employing embeddings for tasks related to text similarity, or evaluating the reasons behind token importance as measured through attribution methods. Applications for embedding exploration frequently involve dimensionality reduction techniques, which reduce high-dimensional vectors to two dimensions used as coordinates in a scatterplot. This data transformation step introduces uncertainty that can be propagated to the visual representation and influence users' interpretation of the data. To communicate such uncertainties, we present LayerFlow - a visual analytics workspace that displays embeddings in an interlinked projection design and communicates the transformation, representation, and interpretation uncertainty. In particular, to hint at potential data distortions and uncertainties, the workspace includes several visual components, such as convex hulls showing 2D and HD clusters, data point pairwise distances, cluster summaries, and projection quality metrics. We show the usability of the presented workspace through replication and expert case studies that highlight the need to communicate uncertainty through multiple visual components and different data perspectives.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。