arXiv:2504.11837v3 Announce Type: replace Abstract: Emotional support conversation (ESC) aims to alleviate people's emotional distress through effective conversations. Although large language models (LLMs) have made remarkable progress in ESC, most of these studies may not define the diagram from a state-model perspective, thereby providing a suboptimal solution for long-term satisfaction. To address such an issue, we leverage the Finite State Machine (FSM) on LLMs, and propose a framework called EmoFSM. Our framework allows a single LLM to bootstrap the planning during ESC, and self-reason th
The rapid advancement of large language models (LLMs) has highlighted their current limitations in structured, long-term interaction, prompting researchers to seek more robust frameworks for complex tasks like emotional support.
This development suggests a pathway to more reliable, predictable, and effective AI interactions, moving beyond purely probabilistic responses to state-aware, goal-oriented conversational systems.
The explicit incorporation of Finite State Machines (FSMs) into LLM planning and self-reasoning changes how complex, multi-turn AI conversations can be designed and executed, offering more control and consistency.
- · AI-driven customer support platforms
- · Mental health tech companies
- · AI framework developers
- · Conversational AI researchers
- · Platforms relying solely on unstructured LLM outputs for complex tasks
- · Developers without expertise in state-based system design
Emotional support chatbots become significantly more consistent and effective in their long-term interactions.
The application of FSM principles extends to other complex AI agentic tasks, improving reliability and reducing 'hallucinations' in sequential operations.
This structured approach to AI interaction design could lead to a 'design language' for AI systems, making their behavior more auditable and predictable, and ultimately accelerate human-AI teaming in critical domains.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL