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Grip on LLMs: Benchmarking Large Language Models for Dutch Government Use

Forum topic · 小凯 · 2026-08-11

Summary

This paper introduces Grip on LLMs, a systematic evaluation framework for assessing large language models in Dutch governmental settings. Developed with domain experts from a major Dutch municipal organisation, the framework uses an advisory board process, user research, and surveys of civil-servant chatbot users to identify six evaluation dimensions: factuality, honesty, social bias, energy consumption, cost, and training data transparency. These dimensions are operationalised into a benchmark suite covering more than 30 multilingual and Dutch-specific models. The results show that no single model excels across all dimensions—higher quality consistently comes with greater environmental and financial cost, while bias levels are largely independent of these factors. Importantly, factuality and honesty are driven by different model properties, so high factuality does not imply high honesty. A public, user-friendly model overview is released to support stakeholders ranging from engineers to policymakers. arXiv:2508.03803.

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
  • 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 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.
  • 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*

    Key Points

  • 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

Tags

#llm-evaluation#dutch-language#government-ai#benchmark#nlp#ai-governance#arxiv#multilingual-models

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