From Queries to Conversations: Pragmatic and Interdependent Human–LLM Collaboration in Data Work
Co-Data Workshop at CHI 2026: Cultivating Effective Human–LLM Collaboration for Collaborative Data Processing
Data-intensive workflows are rarely solitary endeavors. Analysts, domain experts, and data engineers collaborate to collect, clean, integrate, annotate, and query data, often negotiating meaning, intent, and responsibility through conversation. As Large Language Models (LLMs) are increasingly embedded in data tools, their role is typically framed as responding to isolated queries or automating discrete steps. We argue that this framing underutilizes LLMs’ potential and obscures important challenges in collaborative data work. In this position paper, we propose treating LLMs as pragmatic collaborators rather than tools, wherein participants that engage in ongoing conversations, mediate semantic differences, and adapt their behavior based on interdependence among stakeholders. Grounded in Gricean Maxims and Interdependence Theory, we examine how LLMs can support intent refinement, handle vagueness, and maintain discourse coherence across multi-party data workflows. We focus on semantic misalignment and goal misalignment as recurring sources of breakdown, and identify failure cases where violations of pragmatic norms undermine trust and shared outcomes.
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