LLM-Augmented Semantic Steering of Text Embedding Projection Spaces
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...
| Authors | Wei Liu, Eric Krokos, Kirsten Whitley et al. |
|---|---|
| Primary category | cs.HC |
| Published | 3 May 2026 |
| Last updated at source | 3 May 2026 |
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