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.

通过多级翻译、语义嵌入扩展和基于用户轮廓的增强技术，该论文探讨了一种在用户的词汇语义空间中改善跨语言信息检索系统召回率的新技术。实验结果表明，该方法在新闻和 Twitter 数据集上相对于基线 BM25 排序在 ROUGE 指标方面表现出优越性，并且该翻译方法在多步骤过程中保持了语义的准确性。此个性化跨语言信息检索框架有望改善对用户语言细微差别的感知性。

利用翻译优化召回率：通过用户配置个性化 LLM

Leveraging Translation For Optimal Recall: Tailoring LLM Personalization  With User Profiles

This paper presents first successful steps in designing search agents that
learn meta-strategies for iterative query refinement in information-seeking
tasks. Our approach uses machine reading to guide the selection of refinement
terms from aggregated search results. Agents are then empowered with simple but
effective search operators to exert fine-grained and transparent control over
queries and search results. We develop a novel way of generating synthetic
search sessions, which leverages the power of transformer-based language models
through (self-)supervised learning. We also present a reinforcement learning
agent with dynamically constrained actions that learns interactive search
strategies from scratch. Our search agents obtain retrieval and answer quality
performance comparable to recent neural methods, using only a traditional
term-based BM25 ranking function and interpretable discrete reranking and
filtering actions.

本文介绍了设计搜索代理的成功步骤，通过机器学习元策略来迭代查询精细化。文章提出了一种新颖的方法，使用机器阅读辅助挑选查询结果的精炼术语，使代理能够通过简单而有效的搜索操作对查询和搜索结果进行细粒度和透明的控制。通过自主学习，利用基于 Transformer 的语言模型来生成综合搜索会话，并介绍了一种强化学习代理，其具有动态限制的动作，可以从零开始学习交互式搜索策略。我们的搜索代理只使用传统的基于词项的 BM25 排名函数和可解释的离散筛选和筛选操作，即可达到与最新的神经方法相当的检索和答案质量性能。