arXiv:2606.26595v1 Announce Type: new Abstract: Enhancing the analysis of service feedback is essential for public sector organizations, particularly tax administrations, where trust and compliance depend on fair and effective service delivery. As feedback volumes grow, identifying emerging service quality issues and potential disparities across diverse populations becomes increasingly challenging. Traditional approaches often rely on manual review or static expert-defined indicators, limiting scalability and the ability to capture complex patterns in textual feedback. This paper presents a no
The proliferation of LLMs and increasing volumes of digital feedback are converging, making AI-driven analysis critical for organizational efficiency and public trust.
This development allows public sector organizations, particularly tax administrations, to rapidly identify and address emerging service issues, improving public trust and operational effectiveness which is critical for smooth governance.
Traditionally manual or static feedback analysis within government entities can now be automated and dynamically updated, providing real-time insights into public sentiment and service delivery gaps.
- · Public sector organizations (e.g., tax administrations)
- · AI/LLM developers
- · Citizens receiving public services
- · Consulting firms specializing in public sector digital transformation
- · Providers of traditional feedback analysis software
- · Manual data analysts in public sector
- · Inefficient bureaucracies
Public sector organizations gain enhanced visibility into service quality issues and disparities.
Improved public services and trust in government institutions lead to greater compliance and citizen satisfaction.
The successful application in the public sector could accelerate broader adoption of LLM-based tools across diverse governmental and non-governmental service industries, redefining feedback loops and stakeholder engagement.
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