LayerFlow: Layer-wise Exploration of LLM Embeddings using Uncertainty-aware Interlinked Projections

2025-04-09

AI Summary

This paper addresses the challenge of characterizing and interpreting uncertainty in dimensionality reduction of large language model (LLM) embeddings. Methodologically, it introduces an uncertainty-aware, inter-layer coordinated visualization framework that integrates statistical uncertainty modeling with t-SNE/UMAP-based dimensionality reduction. It features a novel multi-view design enabling synchronized layer-wise projections, coupled with convex-hull clustering, pairwise distance plots, and projection quality metrics to jointly reveal high-dimensional semantic/syntactic structures and their 2D projection distortions. Contributions include: (1) the first systematic quantification and visualization of interpretability bias induced by dimensionality reduction; (2) a scalable, interactive embedding analysis workspace; and (3) empirically validated improvements—via replication studies and expert evaluation—in users’ ability to assess embedding reliability and structural interpretability, thereby advancing deployable, trustworthy LLM explainability tools.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Understanding LLM embeddings' semantic and syntactic properties
• Addressing uncertainty in dimensionality reduction visualizations
• Communicating data distortions in embedding projections

Innovation

• Uncertainty-aware interlinked projection design
• Visual components for data distortions
• Multiple perspectives for uncertainty communication