Grip on LLMs: A Benchmark Framework for Dutch Government LLM Evaluation
Field: NLP Authors: Laurens Samson, Iva Gornishka, Gossa Lô Published: 2026-08-12 arXiv: 2508.05157
Overview
Large language models are increasingly being deployed in governmental settings, yet few existing evaluation frameworks jointly reflect the values of public administration and the linguistic requirements of non-English contexts. This paper presents the Grip on LLMs framework, a systematic evaluation suite for Dutch governmental use developed in collaboration with domain experts from a major Dutch municipal organization.
Key Points
- Methodology: Through an advisory board process, user research, and a survey of civil-servant chatbot users, the authors identified six evaluation dimensions:
- Factuality
- Honesty
- Social bias
- Energy consumption
- Cost
- Training data transparency
- Benchmark Coverage: These dimensions were operationalized into a benchmark suite covering more than 30 multilingual and Dutch-specific models.
- Core Findings:
- No single model excels across all dimensions. Trade-offs are unavoidable.
- Quality vs. Cost Trade-off: Higher quality typically means larger environmental impact and higher financial cost.
- Bias Independence: Social bias is largely independent of both quality and cost factors.
- Factuality ≠ Honesty: Factuality (whether the model answers correctly) and honesty (whether the model admits it does not know) are governed by different model attributes. High factuality does not imply high honesty.
- Public Tool: To make the findings accessible to non-technical audiences, the authors released a publicly available, user-friendly model overview tool designed for stakeholders ranging from engineers to policy makers.
Significance
The framework addresses a critical gap by combining public-sector value alignment with non-English linguistic requirements, offering practical guidance for governmental LLM procurement and deployment decisions.
---
*Auto-collected from arXiv on 2026-08-12.*