Collective Narrative Grounding: Community-Coordinated Data Contributions to Improve Local AI Systems
Zihan Gao, Mohsin Y. K. Yousufi, Jacob Thebault-Spieker

TL;DR
This paper introduces Collective Narrative Grounding, a participatory approach that transforms community stories into structured data to improve local AI question-answering systems, addressing knowledge gaps and cultural misunderstandings.
Contribution
It presents a novel participatory protocol for integrating community narratives into AI systems, including schema design, validation methods, and governance considerations.
Findings
76.7% of local QA errors are due to factual gaps and misunderstandings.
State-of-the-art LLMs answer less than 21% of local questions correctly without context.
Narratives collected can directly address many factual and contextual gaps.
Abstract
Large language model (LLM) question-answering systems often fail on community-specific queries, creating "knowledge blind spots" that marginalize local voices and reinforce epistemic injustice. We present Collective Narrative Grounding, a participatory protocol that transforms community stories into structured narrative units and integrates them into AI systems under community governance. Learning from three participatory mapping workshops with N=24 community members, we designed elicitation methods and a schema that retain narrative richness while enabling entity, time, and place extraction, validation, and provenance control. To scope the problem, we audit a county-level benchmark of 14,782 local information QA pairs, where factual gaps, cultural misunderstandings, geographic confusions, and temporal misalignments account for 76.7% of errors. On a participatory QA set derived from our…
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Taxonomy
TopicsTopic Modeling · Expert finding and Q&A systems · Scientific Computing and Data Management
