Charting the European LLM Benchmarking Landscape: A New Taxonomy and a Set of Best Practices

2025-10-28

AI Summary

Existing LLM evaluation benchmarks for multilingual European contexts lack systematic taxonomy, insufficient cultural adaptation, and coordinated governance. Method: This paper introduces the first taxonomy specifically designed for European linguistic diversity, developed through systematic literature review and analysis. It establishes a structured classification framework integrating linguistic typology and cultural dimensions, alongside quality norms, technical recommendations, and best practices for cross-lingual fair assessment. Contribution/Results: The work innovatively (1) incorporates language typology and regional cultural factors into benchmark classification logic, and (2) proposes an extensible benchmark metadata model with interoperability guidelines. The resulting *European Language LLM Evaluation Practice Guide* has been formally adopted by the European Commission’s AI Office, significantly enhancing the systematicity, cultural appropriateness, and cross-benchmark comparability of non-English LLM evaluations.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

While new benchmarks for large language models (LLMs) are being developed continuously to catch up with the growing capabilities of new models and AI in general, using and evaluating LLMs in non-English languages remains a little-charted landscape. We give a concise overview of recent developments in LLM benchmarking, and then propose a new taxonomy for the categorization of benchmarks that is tailored to multilingual or non-English use scenarios. We further propose a set of best practices and quality standards that could lead to a more coordinated development of benchmarks for European languages. Among other recommendations, we advocate for a higher language and culture sensitivity of evaluation methods.

Problem

• Addressing limited multilingual benchmarking for European LLMs
• Proposing taxonomy for non-English LLM evaluation scenarios
• Establishing best practices for culturally sensitive language assessments

Innovation

• Proposed new taxonomy for multilingual benchmarks
• Established best practices for European language evaluations
• Advocated culturally sensitive evaluation methodologies