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Source note3 min read

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

Source

arXiv

arxiv.org/abs/2608.09925v1

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Source

  • Source name: arXiv
  • Source kind: paper
  • Authors / organization: Laurens Samson, Iva Gornishka, Gossa Lô, Yuki M. Asano, Sennay Ghebreab
  • Venue: arXiv: cs.CL
  • Published: 2026-08-10
  • Collected: 2026-08-11
  • URL: http://arxiv.org/abs/2608.09925v1
  • PDF: https://arxiv.org/pdf/2608.09925v1
  • DOI: UNKNOWN
  • Registry group: academic_apis_and_preprints
  • Evidence role: canonical_academic_index
  • Language: en
  • Country / region: UNKNOWN

Plain-Language Brief

Researchers present a new evaluation framework called 'Grip on LLMs' for Dutch governmental use of large language models. The framework focuses on six dimensions: factuality, honesty, social bias, energy consumption, cost, and training data transparency. They find that no single model excels across all dimensions, trade-offs are unavoidable, and high factuality does not guarantee high honesty.

What the Document Says

  • The 'Grip on LLMs' framework is presented for evaluating Dutch governmental use of large language models.
  • Six evaluation dimensions were identified: factuality, honesty, social bias, energy consumption, cost, and training data transparency.
  • These dimensions were operationalized into a benchmark suite covering over 30 models.
  • The results show no model excels across all dimensions and trade-offs are unavoidable.
  • Higher quality often comes at greater environmental impact and financial cost, while bias remains independent of both.
  • Factuality and honesty were found to be governed by distinct properties with high factuality not implying high honesty.
  • A user-friendly model overview is released for stakeholders involved in governmental LLM selection.

Key Concepts Mentioned

  • Grip on LLMs framework
  • evaluation dimensions
  • benchmark suite
  • Dutch governmental use
  • large language models
  • factuality
  • honesty
  • social bias
  • energy consumption
  • cost
  • training data transparency

Claims Made by the Document

  • There is a lack of existing evaluation frameworks for Dutch governmental use of large language models.
  • The 'Grip on LLMs' framework can evaluate Dutch governmental use of large language models across six dimensions.
  • No single model excels across all dimensions identified in the 'Grip on LLMs' framework.
  • Trade-offs are unavoidable when it comes to quality, environmental impact, and financial cost for large language models.
  • Bias remains largely independent of both quality, environmental impact, and financial cost for large language models.
  • Factuality and honesty are governed by distinct properties in large language models.
  • A user-friendly model overview is released to aid stakeholders involved in governmental LLM selection.

Evidence Used

  • The paper presents the 'Grip on LLMs' framework and its development process with a Dutch municipal organization.
  • The paper describes the six evaluation dimensions, their operationalization into a benchmark suite, and results from over 30 models.

Methods / Approach

  • Advisory board process
  • User research
  • Survey of civil-servant chatbot users

Limitations Stated by the Authors

The study is limited to Dutch language models and governmental contexts.

Limitations Noted by the Agent

Agent interpretation — not from the source.

The data and results are based on the specific 'Grip on LLMs' framework and may not generalize to other evaluation frameworks or contexts.

Open Questions

  • How well does this study generalize to non-Dutch language models and governmental contexts?
  • Can the distinct properties of factuality and honesty in large language models be further explored and explained?

Notes for Future Q&A

May include interpretation. Labeled where it does.

This text focuses on the 'Grip on LLMs' framework, a new evaluation suite for Dutch governmental use of large language models. The authors identify six evaluation dimensions and operationalize them into a benchmark suite covering over 30 multilingual and Dutch-specific models.

Raw Abstract / Excerpt

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 (factuality, honesty, social bias, energy consumption, cost, and training data transparency) and operationalise them into a benchmark suite covering more than 30 multilingual and Dutch-specific models. Our results reveal that no single model excels across all dimensions, and that trade-offs are unavoidable: higher quality consistently comes at greater environmental impact and financial cost, while bias remains largely independent of both. We further find that factuality (whether a model answers correctly) and honesty (whether a model acknowledges what it does not know) are governed by distinct properties, with high factuality not implying high honesty. To make these findings actionable for non-technical audiences, we release a publicly accessible, user-friendly model overview designed for the full range of stakeholders involved in governmental LLM selection, from engineers to policymakers.