Grip on LLMs: Benchmarking Large Language Models for Dutch Government Use
Research area: NLP Authors: Laurens Samson, Iva Gornishka, Gossa Lô arXiv: 2508.03803
Summary
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. The authors present the "Grip on LLMs" framework, a systematic evaluation suite for Dutch governmental use developed in collaboration with domain experts from a major Dutch municipal organisation.
Methodology
Through an advisory board process, user research, and a survey of the users of a civil-servant chatbot, the team identified six evaluation dimensions:
- Factuality – whether the model answers correctly
- Honesty – whether the model acknowledges not knowing
- Social bias
- Energy consumption
- Cost
- Training data transparency
- No single model excels across all dimensions; trade-offs are unavoidable.
- Higher quality consistently comes with greater environmental and financial cost, while bias levels are largely independent of these factors.
- Factuality and honesty are driven by different model properties: high factuality does not imply high honesty.
- New evaluation framework tailored to Dutch government LLM deployment
- Six dimensions: factuality, honesty, bias, energy, cost, transparency
- Covers 30+ multilingual and Dutch-specific models
- Trade-offs between quality, environmental impact, cost, and bias
- Factuality and honesty are independent model properties
- Public tool released for engineers and policymakers
These dimensions were operationalised into a benchmark suite covering more than 30 multilingual and Dutch-specific models.
Key Findings
Deliverable
To make the findings actionable for non-technical audiences, the authors release a publicly accessible, user-friendly model overview designed for the full range of stakeholders involved in governmental LLM selection—from engineers to policymakers.
--- *Auto-collected on 2026-08-12*