Semantic Prompting: Agentic Incremental Narrative Refinement through Spatial Semantic Interaction
This work addresses incremental spatial refinement for sensemaking in human-LLM collaboration, though it appears incremental as it builds on existing methods to fill specific gaps.
The paper tackled the problem of misalignment between user interactions and LLM revisions in spatial-textual narrative generation by introducing Semantic Prompting, which improved interaction-revision precision and was valued by users for efficient and trustworthy support in a study with 14 participants.
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.