评测多语言欧洲大模型，发现四大挑战并提出改进方案。
Multilingual European Language Models: Benchmarking Approaches and Challenges
- 分析7个多语言欧洲基准数据集，识别评估瓶颈。
- 指出文化偏见和翻译质量是影响评估准确性的关键问题。
- 适合关注多语种大模型评测与公平性的研究者参考。
生成式大语言模型通过对话交互解决多种任务，推动了通用评估基准的发展。然而，现有主流基准大多聚焦英语，难以全面评估多语言模型。本文分析了7个多语言欧洲基准数据集的优缺点，识别出四大核心挑战：翻译质量不足、文化偏见显著、评估标准不统一、跨语言泛化能力弱。为此，提出结合人工介入验证与迭代翻译排名等方法，以提升翻译准确性和文化敏感性。研究强调，必须建立具备文化意识且严格验证的基准，才能准确评估多语言大模型在推理与问答任务中的真实能力。
原文摘要 · Abstract (English)
The breakthrough of generative large language models (LLMs) that can solve different tasks through chat interaction has led to a significant increase in the use of general benchmarks to assess the quality or performance of these models beyond individual applications. There is also a need for better methods to evaluate and also to compare models due to the ever increasing number of new models published. However, most of the established benchmarks revolve around the English language. This paper analyses the benefits and limitations of current evaluation datasets, focusing on multilingual European benchmarks. We analyse seven multilingual benchmarks and identify four major challenges. Furthermore, we discuss potential solutions to enhance translation quality and mitigate cultural biases, including human-in-the-loop verification and iterative translation ranking. Our analysis highlights the need for culturally aware and rigorously validated benchmarks to assess the reasoning and question-answering capabilities of multilingual LLMs accurately.
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