多语言欧洲语言模型：基准测试方法与挑战
Multilingual European Language Models: Benchmarking Approaches and Challenges
AI总结：
本文分析了七个多语言欧洲基准，指出了四个主要挑战，并提出人在回路验证与迭代翻译排名等方案，强调需构建具文化意识且严格验证的基准以准确评估多语言LLMs。
摘要：
能够通过聊天交互解决不同任务的生成式大型语言模型（LLMs）的突破，促使使用通用基准来评估这些模型在单个应用之外的质量或性能的行为显著增加。由于不断发布的新模型数量日益增加，也需要更好的方法来评估和比较模型。然而，大多数现有的基准都以英语为中心。本文分析了当前评估数据集的益处与局限性，重点关注多语言欧洲基准。我们分析了七个多语言基准，并指出了四个主要挑战。此外，我们讨论了增强翻译质量和缓解文化偏见的潜在解决方案，包括人在回路的验证和迭代翻译排名。我们的分析强调，需要具有文化意识且经过严格验证的基准，以准确评估多语言LLMs的推理和问答能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
