Hakim：波斯语文本嵌入模型
Hakim: Farsi Text Embedding Model
AI总结：
本文提出波斯语文本嵌入模型Hakim，在FaMTEB基准上提升8.5%，并引入三个新数据集及基于BERT的基线，适用于聊天机器人和RAG系统。
摘要：
文本嵌入领域的最新进展显著提升了多种语言的自然语言理解能力，然而波斯语在大规模嵌入研究中仍明显代表性不足。在本文中，我们提出了Hakim，一种新颖的最先进的波斯语文本嵌入模型，在FaMTEB基准测试上相比现有方法实现了8.5%的性能提升，超越了所有先前开发的波斯语语言模型。作为这项工作的一部分，我们引入了三个新数据集——Corpesia、Pairsia-sup和Pairsia-unsup——以支持监督和无监督训练场景。此外，Hakim专为聊天机器人和检索增强生成（RAG）系统中的应用而设计，特别针对这些系统中需要整合消息历史的检索任务。我们还提出了一个基于BERT架构的新基线模型。我们的语言模型在各种波斯语NLP任务中持续实现更高的准确率，而基于RetroMAE的模型在文本信息检索应用中尤其有效。这些贡献共同为推进波斯语理解建立了新的基础。
英文摘要：
Recent advancements in text embedding have significantly improved natural language understanding across many languages, yet Persian remains notably underrepresented in large-scale embedding research. In this paper, we present Hakim, a novel state-of-the-art Persian text embedding model that achieves a 8.5% performance improvement over existing approaches on the FaMTEB benchmark, outperforming all previously developed Persian language models. As part of this work, we introduce three new datasets - Corpesia, Pairsia-sup, and Pairsia-unsup - to support supervised and unsupervised training scenarios. Additionally, Hakim is designed for applications in chatbots and retrieval-augmented generation (RAG) systems, particularly addressing retrieval tasks that require incorporating message history within these systems. We also propose a new baseline model built on the BERT architecture. Our language model consistently achieves higher accuracy across various Persian NLP tasks, while the RetroMAE-based model proves particularly effective for textual information retrieval applications. Together, these contributions establish a new foundation for advancing Persian language understanding.
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。
