利用翻译实现最优召回：通过用户画像定制大语言模型个性化
Leveraging Translation For Optimal Recall: Tailoring LLM Personalization With User Profiles
AI总结：
本文提出一种个性化跨语言信息检索方法，通过多级翻译、语义嵌入扩展和用户画像增强迭代优化查询，在新闻和Twitter数据集上超越BM25基线，提升了召回率。
摘要：
本文探索了一种在跨语言信息检索（CLIR）系统中利用基于用户词汇-语义空间的迭代查询优化来提高召回率的新技术。所提出的方法结合了多级翻译、基于语义嵌入的扩展以及以用户画像为中心的增强，以应对用户查询与相关文档之间的匹配差异问题。通过初始 BM25 检索、翻译为中间语言、相似术语的嵌入查找以及迭代重排序，该技术旨在扩大针对个体用户个性化的潜在相关结果范围。在新闻和 Twitter 数据集上的对比实验表明，所提出的方法在 ROUGE 指标上优于基线 BM25 排序。该翻译方法还在多步骤过程中保持了语义准确性。这一个性化 CLIR 框架为改进上下文感知检索铺平了道路，使其能够关注用户语言的细微差别。
英文摘要：
This paper explores a novel technique for improving recall in cross-language information retrieval (CLIR) systems using iterative query refinement grounded in the user's lexical-semantic space. The proposed methodology combines multi-level translation, semantic embedding-based expansion, and user profile-centered augmentation to address the challenge of matching variance between user queries and relevant documents. Through an initial BM25 retrieval, translation into intermediate languages, embedding lookup of similar terms, and iterative re-ranking, the technique aims to expand the scope of potentially relevant results personalized to the individual user. Comparative experiments on news and Twitter datasets demonstrate superior performance over baseline BM25 ranking for the proposed approach across ROUGE metrics. The translation methodology also showed maintained semantic accuracy through the multi-step process. This personalized CLIR framework paves the path for improved context-aware retrieval attentive to the nuances of user language.
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。
