Semantic Prompting: Agentic Incremental Narrative Refinement through Spatial Semantic Interaction
arXiv:2604.19971v2 Announce Type: replace-cross 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 introduc
The proliferation of LLMs creates a demand for more refined, interactive human-AI interfaces for complex tasks like sensemaking, pushing research into advanced prompting and interaction paradigms.
Improving how humans interact with and refine LLM outputs for information synthesis is crucial for increasing the utility and trustworthiness of AI in knowledge work.
The development of 'Semantic Prompting' and 'Spatial Semantic Interaction' suggests a move beyond basic text prompts towards more intuitive, iterative, and spatially-aware methods for guiding LLMs.
- · Knowledge workers
- · UX designers for AI applications
- · AI research labs focusing on human-AI interaction
- · LLM application developers
- · Tools relying solely on static, one-shot LLM prompting
- · Traditional text-based knowledge synthesis methods
Improved human-LLM collaboration for complex analytical tasks and narrative generation.
Accelerated development cycles for research, intelligence, and content creation by automating iterative refinement.
Potential for new cognitive biases to emerge from overly smooth or guided AI-assisted sensemaking processes.
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Read at arXiv cs.AI