弥合语言鸿沟：基于多语言大语言模型的跨语言信息检索研究进展
Bridging Language Gaps: Advances in Cross-Lingual Information Retrieval with Multilingual LLMs
AI总结：
本综述梳理了跨语言信息检索从基于翻译到嵌入驱动、生成式技术的发展，介绍其核心组件、评估与资源，指出现存挑战并提出未来发展方向。
摘要：
跨语言信息检索（CLIR）旨在解决检索与原始查询语言不同的相关文档的挑战。该领域的研究通常将该任务构建为经翻译增强的单语言检索，将检索方法与跨语言能力分开处理。单语言和跨语言检索通常都遵循查询扩展、排序、重排序以及日益增多的问答这一流程。然而，近期的研究进展已从基于翻译的方法转向基于嵌入的方法，并利用多语言大语言模型（LLMs），对这类模型而言，跨语言表示对齐仍是核心挑战。跨语言嵌入和多语言LLMs的出现催生了一种新范式，既提升了检索性能，又支持答案生成。本综述全面梳理了从早期基于翻译的方法到最先进的嵌入驱动与生成式技术的发展历程，结构化介绍了CLIR的核心组件、评估实践以及可用资源。本文指出了数据不平衡、语言差异等长期存在的挑战，并提出了推动公平、有效的跨语言信息检索发展的可行方向。通过将CLIR置于信息检索和多语言语言处理的更广阔背景下，本研究不仅回顾了当前的能力，还为构建鲁棒、包容、可适配的检索系统勾勒了未来方向。
英文摘要：
Cross-lingual information retrieval (CLIR) addresses the challenge of retrieving relevant documents written in languages different from that of the original query. Research in this area has typically framed the task as monolingual retrieval augmented by translation, treating retrieval methods and cross-lingual capabilities in isolation. Both monolingual and cross-lingual retrieval usually follow a pipeline of query expansion, ranking, re-ranking and, increasingly, question answering. Recent advances, however, have shifted from translation-based methods toward embedding-based approaches and leverage multilingual large language models (LLMs), for which aligning representations across languages remains a central challenge. The emergence of cross-lingual embeddings and multilingual LLMs has introduced a new paradigm, offering improved retrieval performance and enabling answer generation. This survey provides a comprehensive overview of developments from early translation-based methods to state-of-the-art embedding-driven and generative techniques. It presents a structured account of core CLIR components, evaluation practices, and available resources. Persistent challenges such as data imbalance and linguistic variation are identified, while promising directions are suggested for advancing equitable and effective cross-lingual information retrieval. By situating CLIR within the broader landscape of information retrieval and multilingual language processing, this work not only reviews current capabilities but also outlines future directions for building retrieval systems that are robust, inclusive, and adaptable.
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。
