标题： 文本到Cypher跨语言：评估超越英语的基础模型 显示英文标题

标题： Text2Cypher Across Languages: Evaluating Foundational Models Beyond English

摘要： 最近大型语言模型的进展使得自然语言接口成为可能，这些接口可以将用户的问题转化为数据库查询，例如Text2SQL、Text2SPARQL和Text2Cypher。 尽管这些接口增强了数据库的可访问性，但目前大多数研究仅关注英语，其他语言的评估有限。 本文研究了基础LLM在多种语言上的Text2Cypher任务表现。 我们通过将英语问题翻译成西班牙语和土耳其语而保留原始Cypher查询，创建并发布了多语言测试集，从而实现了公平的跨语言比较。 我们使用标准化提示和指标对多个基础模型进行了评估。 我们的结果显示出一致的表现模式：英语表现最高，其次是西班牙语，最后是土耳其语。 我们认为这是由于训练数据可用性和语言特征的差异造成的。 此外，我们探讨了将任务提示翻译成西班牙语和土耳其语的影响。 结果表明评估指标几乎没有变化，这表明提示翻译影响较小。 我们的发现强调了在多语言查询生成中需要更加包容的评估和开发。 未来的工作包括模式本地化和在多种语言上的微调。 显示英文摘要

摘要： Recent advances in large language models have enabled natural language interfaces that translate user questions into database queries, such as Text2SQL, Text2SPARQL, and Text2Cypher. While these interfaces enhance database accessibility, most research today focuses solely on English, with limited evaluation in other languages. This paper investigates the performance of foundational LLMs on the Text2Cypher task across multiple languages. We create and release a multilingual test set by translating English questions into Spanish and Turkish while preserving the original Cypher queries, enabling fair cross-lingual comparison. We evaluate multiple foundational models using standardized prompts and metrics. Our results show a consistent performance pattern: highest on English, then Spanish, and lowest on Turkish. We attribute this to differences in training data availability and linguistic characteristics. Additionally, we explore the impact of translating task prompts into Spanish and Turkish. Results show little to no change in evaluation metrics, suggesting prompt translation has minor impact. Our findings highlight the need for more inclusive evaluation and development in multilingual query generation. Future work includes schema localization and fine-tuning across diverse languages.