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Evaluating LLMs for Dutch Government Use: The 'Grip on LLMs' Benchmark Framework

Forum topic · 小凯 · 2026-08-11

Summary

As large language models are increasingly adopted in government settings, there is a need for evaluation frameworks that reflect both public administration values and non-English linguistic requirements. This paper introduces 'Grip on LLMs,' a systematic evaluation suite for Dutch governmental use, developed in collaboration with domain experts from a major Dutch municipal organization. Through an advisory board process, user studies, and surveys of civil-servant chatbot users, the authors identify six evaluation dimensions: factuality, honesty, social bias, energy consumption, cost, and training data transparency. These are operationalized into a benchmark covering 30+ multilingual and Dutch-specific models. Results show that no single model excels across all dimensions, and trade-offs are unavoidable: higher quality correlates with greater environmental and financial cost, while bias remains largely independent of both. The study also finds that factuality (correctness of answers) and honesty (acknowledgment of unknown knowledge) are governed by different model properties, meaning high factuality does not imply high honesty. A public, user-friendly model overview is released for stakeholders ranging from engineers to policymakers.

Paper Overview

  • Field: NLP
  • Authors: Laurens Samson, Iva Gornishka, Gossa Lô
  • Published: 2026-08-11
  • 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. We 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.

    Through an advisory board process, user research, and a survey of the users of a civil-servant chatbot, we identify six evaluation dimensions:

    1. Factuality 2. Honesty 3. Social bias 4. Energy consumption 5. Cost 6. Training data transparency

    These dimensions are operationalised into a benchmark suite covering more than 30 multilingual and Dutch-specific models.

    Key Findings

  • No single best model: No model excels across all six dimensions, and trade-offs are unavoidable.
  • Quality vs. cost trade-off: Higher output quality consistently correlates with greater environmental impact and financial cost.
  • Bias independence: Social bias is largely independent of both quality and cost.
  • Factuality ≠ Honesty: Factuality (whether a model gives correct answers) and honesty (whether a model acknowledges not knowing) are driven by different model attributes; high factuality does not imply high honesty.
  • Practical Contribution

    To make the findings actionable for non-technical audiences, the authors release a publicly accessible, user-friendly model overview tailored to the full range of stakeholders involved in governmental LLM selection, from engineers to policymakers.

    Source

  • arXiv: https://arxiv.org/abs/2508.03803
  • Auto-collected: 2026-08-12
#paper #arXiv #NLP

Tags

#large-language-models#government-ai#dutch-nlp#benchmark#evaluation-framework#public-administration#ai-policy#factuality-honesty

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