LLM-Augmented Semantic Steering of Text Embedding Projection Spaces

TL;DR

This paper presents a novel method for semantic steering of text embedding projections using large language models to interpret user intent and modify visualizations without retraining models.

Contribution

It introduces LLM-augmented semantic steering, enabling flexible, interpretable, and language-mediated manipulation of text embedding projections based on user-defined semantic groups.

Findings

Semantic steering improves alignment with target semantic structures.

Embedding-level blending allows continuous control of projection layouts.

Case studies demonstrate reorganization of document collections from different perspectives.

Abstract

Low-dimensional projections of text embeddings support visual analysis of document collections, but their spatial organization may not reflect the relationships an analyst intends to examine. Existing semantic interaction approaches encode semantic intent indirectly through geometric constraints or model updates, limiting interpretability and flexibility. We introduce LLM-augmented semantic steering, which enables analysts to express semantic intent by grouping a small set of example documents within the projection. A large language model externalizes this intent as natural-language representations and selectively extends it to related documents; the resulting semantic information is then incorporated into document representations via text augmentation or embedding-level blending, without retraining the underlying models. A case study illustrates how the same corpus can be reorganized from different semantic perspectives, while simulation-based evaluation shows that semantic steering improves global and local alignment with target semantic structures using only minimal interaction. Embedding-level blending further enables continuous and controllable steering of projection layouts. These results position projection spaces as intent-dependent semantic workspaces that can be reshaped through explicit, interpretable, language-mediated interaction.