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Fictional Worlds, Real Connections: Developing Community Storytelling Social Chatbots through LLMs

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arxiv 2309.11478 v1 pith:J6346ENE submitted 2023-09-20 cs.AI

classification cs.AI
keywords communitysocialstorytellingchatbotsfictionalstorycharactersengineering
verification ladder T0 review T1 audit T2 compute T3 formal
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We address the integration of storytelling and Large Language Models (LLMs) to develop engaging and believable Social Chatbots (SCs) in community settings. Motivated by the potential of fictional characters to enhance social interactions, we introduce Storytelling Social Chatbots (SSCs) and the concept of story engineering to transform fictional game characters into "live" social entities within player communities. Our story engineering process includes three steps: (1) Character and story creation, defining the SC's personality and worldview, (2) Presenting Live Stories to the Community, allowing the SC to recount challenges and seek suggestions, and (3) Communication with community members, enabling interaction between the SC and users. We employed the LLM GPT-3 to drive our SSC prototypes, "David" and "Catherine," and evaluated their performance in an online gaming community, "DE (Alias)," on Discord. Our mixed-method analysis, based on questionnaires (N=15) and interviews (N=8) with community members, reveals that storytelling significantly enhances the engagement and believability of SCs in community settings.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Drama Llama: An LLM-Powered Storylets Framework for Authorable Responsiveness in Interactive Narrative

    cs.HC 2025-01 conditional novelty 6.0 of 10

    An LLM-powered storylets framework lets authors write natural-language triggers that fire at appropriate moments, supporting responsive interactive narratives with modest authoring effort.

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