Grip on LLMs: A Benchmark Framework for Evaluating LLMs in Dutch Government Use

Forum topic · 小凯 · 2026-08-11

Summary

Researchers Laurens Samson, Iva Gornishka, and Gossa Lô present 'Grip on LLMs', a systematic evaluation framework for large language models deployed in Dutch governmental settings, developed with domain experts from a major Dutch municipal organisation. Combining an advisory board process, user research, and a survey of civil-servant chatbot users, the study identifies six evaluation dimensions: factuality, honesty, social bias, energy consumption, cost, and training data transparency. These are operationalised into a benchmark suite covering more than 30 multilingual and Dutch-specific models, addressing a gap in existing frameworks that rarely reflect both public administration values and non-English linguistic requirements. Key findings include that no single model excels across all dimensions: higher quality consistently entails greater environmental impact and financial cost, while social bias is largely independent of both. Factuality and honesty are driven by distinct model properties, so high factuality does not imply high honesty. To make results actionable for non-technical stakeholders, the authors release a public, user-friendly model overview for everyone involved in government LLM selection, from engineers to policymakers. Paper: arXiv 2508.03803.

From Values to Benchmarks: Evaluating Large Language Models for Governmental Use in Dutch

Overview

Field: NLP
Authors: Laurens Samson, Iva Gornishka, Gossa Lô
Published: 2026-08-11
arXiv: 2508.03803

Abstract

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

Advisory board process, user research, and a survey of civil-servant chatbot users were used to identify evaluation criteria.
Six evaluation dimensions were identified:
These dimensions were operationalised into a benchmark suite covering more than 30 multilingual and Dutch-specific models.

Key Findings

No single model excels across all dimensions — trade-offs are unavoidable.
Higher quality consistently comes with greater environmental impact and financial cost.
Social bias is largely independent of both quality and cost.
Factuality and honesty are driven by different model properties: a model that answers correctly does not necessarily admit when it does not know.
A public, user-friendly model overview has been released to make the results actionable for the full range of stakeholders involved in government LLM selection, from engineers to policymakers.

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*Originally collected on 2026-08-12.*