基于多语言大语言模型的生成式查询扩展用于跨语言信息检索
Generative Query Expansion with Multilingual LLMs for Cross-Lingual Information Retrieval
AI总结：
本研究探讨了多语言大语言模型在跨语言信息检索中的生成式查询扩展效果，发现查询长度和提示技术对性能影响显著，且不同语言间存在显著的检索性能差异。
摘要：
查询扩展是通过添加语义相关的信息来重新表述用户查询，是单语言和跨语言信息检索中的关键组成部分，用于确保相关文档不被遗漏。最近，多语言大语言模型（mLLMs）将查询扩展从用同义词和相关词进行语义增强转向了伪文档生成。伪文档不仅引入了额外的相关术语，还弥补了短查询和长文档之间的差距，这在密集检索中尤其有益。本研究评估了最近的mLLMs及其微调变体在几种生成扩展策略中的表现，以确定驱动跨语言检索性能的因素。结果显示，查询长度在很大程度上决定了哪种提示技术是有效的，而更复杂的提示通常不会带来进一步的提升。显著的语义差异仍然存在：跨语言查询扩展可以为基线最弱的语言产生最大的改进，但不同书写体系之间的检索效果尤其糟糕。发现微调只有在训练和测试数据格式相似时才能带来性能提升。这些结果强调了对更平衡的多语言和跨语言训练和评估资源的迫切需求。
英文摘要：
Query expansion is the reformulation of a user query by adding semantically related information, and is an essential component of monolingual and cross-lingual information retrieval used to ensure that relevant documents are not missed. Recently, multilingual large language models (mLLMs) have shifted query expansion from semantic augmentation with synonyms and related words to pseudo-document generation. Pseudo-documents both introduce additional relevant terms and bridge the gap between short queries and long documents, which is particularly beneficial in dense retrieval. This study evaluates recent mLLMs and fine-tuned variants across several generative expansion strategies to identify factors that drive cross-lingual retrieval performance. Results show that query length largely determines which prompting technique is effective, and that more elaborate prompts often do not yield further gains. Substantial linguistic disparities persist: cross-lingual query expansion can produce the largest improvements for languages with the weakest baselines, yet retrieval is especially poor between languages written in different scripts. Fine-tuning is found to lead to performance gains only when the training and test data are of similar format. These outcomes underline the need for more balanced multilingual and cross-lingual training and evaluation resources.
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。
