Semantic Prompting: Agentic Incremental Narrative Refinement through Spatial Semantic Interaction

TL;DR

Semantic Prompting introduces a framework for spatial refinement in narrative generation, enabling more precise, incremental, and user-aligned updates in LLM-driven sensemaking tasks.

Contribution

It addresses key gaps in spatial-textual generation by perceiving semantic interactions and performing targeted revisions, implemented in the S-PRISM system.

Findings

S-PRISM improves interaction-revision precision.

Users effectively leverage S-PRISM for incremental formalization.

Participants valued S-PRISM's efficiency and trustworthiness.

Abstract

Interactive spatial layouts empower users to synthesize information and organize findings for sensemaking. While Large Language Models (LLMs) can automate narrative generation from spatial layouts, current collage-based and re-generation methods struggle to support the incremental spatial refinements inherent to the sensemaking process. We identify three critical gaps in existing spatial-textual generation: interaction-revision misalignment, human-LLM intent misalignment, and lack of granular customization. To address these, we introduce Semantic Prompting, a framework for spatial refinement that perceives semantic interactions, reasons about refinement intent, and performs targeted positional revisions. We implemented S-PRISM to realize this framework. The empirical evaluation demonstrated that S-PRISM effectively enhanced the precision of interaction-revision refinement. A user study ($N=14$) highlighted how participants leveraged S-PRISM for incremental formalization through interactive steering. Results showed that users valued its efficient, adaptable, and trustworthy support, which effectively strengthens human-LLM intent alignment.