📝 SummarXiv

Abstract

Effective collaboration with generative AI systems requires users to clearly communicate their intents (intent-based outcome specification). Yet such intents are often underspecified and evolve during interaction, dynamic support for intent communication is essential. Through a systematic literature review of 33 papers, we synthesize a structured understanding of intent communication, identifying four key aspects: articulation, exploration, management, and synchronization. Building on these findings, we derived design implications that translate them into actionable design and implemented IntentFlow, a system for LLM-based writing that realizes these implications through adjustable UIs, intent-to-output linking, and versioned refinement. A technical evaluation (N=60) and a within-subjects study (N=12) confirm that IntentFlow helps users discover, elaborate, and consolidate their intents into a curated set. Interaction logs further reveal a shift from reactive error correction to proactive intent refinement. Our work demonstrates how a system effectively designed to support these four communication aspects can substantially enhance human-LLM interaction.