Gemini Embedding：来自 Gemini 的通用嵌入
计算与语言
2025-03-12 v1 人工智能
摘要
本报告介绍 Gemini Embedding，一种基于 Gemini——谷歌最强大型语言模型的领先级嵌入模型。凭借 Gemini 内在的多语言和代码理解能力，Gemini Embedding 为跨越众多语言和文本模态的文本生成高度通用的嵌入表示。Gemini Embedding 生成的表示可以预计算，并应用于包括分类、相似性、聚类、排序和检索在内的各种下游任务。我们在包含250多个语言中超过一百项任务的大规模多语言文本嵌入基准（MMTEB）上对其进行评估，Gemini Embedding 在嵌入质量方面显著优于先前的领先模型，展示了显著的性能提升。实现 MMTEB 多语言、英语和代码基准的最先进性能，统一模型在广泛的任务选择方面展现出强大的能力，超越了专门的领域特定模型。
引用
@article{arxiv.2503.07891,
title = {Gemini Embedding: Generalizable Embeddings from Gemini},
author = {Jinhyuk Lee and Feiyang Chen and Sahil Dua and Daniel Cer and Madhuri Shanbhogue and Iftekhar Naim and Gustavo Hernández Ábrego and Zhe Li and Kaifeng Chen and Henrique Schechter Vera and Xiaoqi Ren and Shanfeng Zhang and Daniel Salz and Michael Boratko and Jay Han and Blair Chen and Shuo Huang and Vikram Rao and Paul Suganthan and Feng Han and Andreas Doumanoglou and Nithi Gupta and Fedor Moiseev and Cathy Yip and Aashi Jain and Simon Baumgartner and Shahrokh Shahi and Frank Palma Gomez and Sandeep Mariserla and Min Choi and Parashar Shah and Sonam Goenka and Ke Chen and Ye Xia and Koert Chen and Sai Meher Karthik Duddu and Yichang Chen and Trevor Walker and Wenlei Zhou and Rakesh Ghiya and Zach Gleicher and Karan Gill and Zhe Dong and Mojtaba Seyedhosseini and Yunhsuan Sung and Raphael Hoffmann and Tom Duerig},
journal= {arXiv preprint arXiv:2503.07891},
year = {2025}
}
备注
19 pages