标题： 弥合语言鸿沟：多语言大语言模型在跨语言信息检索中的进展 显示英文标题

标题： Bridging Language Gaps: Advances in Cross-Lingual Information Retrieval with Multilingual LLMs

摘要： 跨语言信息检索（CLIR）解决了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任务框架化为通过翻译增强的单语言检索，将检索方法和跨语言能力孤立处理。 单语言和跨语言检索通常遵循查询扩展、排序、重新排序以及越来越多的问题回答的流程。 然而，最近的进展已从基于翻译的方法转向基于嵌入的方法，并利用多语言大型语言模型（LLMs），其中跨语言表示对齐仍然是一个核心挑战。 跨语言嵌入和多语言LLMs的出现引入了一种新范式，提供了改进的检索性能并实现了答案生成。 本综述全面概述了从早期基于翻译的方法到最先进的基于嵌入和生成技术的发展。 它对核心CLIR组件、评估实践和可用资源进行了结构化的描述。 识别了数据不平衡和语言差异等持续性挑战，同时提出了推进公平且有效的跨语言信息检索的有前景的方向。 通过将CLIR置于信息检索和多语言语言处理的更大背景下，这项工作不仅回顾了当前的能力，还概述了构建稳健、包容和适应性强的检索系统的未来方向。 显示英文摘要

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.