A Thorough Examination on Zero-shot Dense Retrieval 论文链接:https://arxiv.org/pdf/2204.12755.pdf 引言 随着预训练语言模型在自然语言处理领域的蓬勃发展,基于预训练语言模型的稠密检索(dense retrieval)近年来也变成了主流的一阶段检索(召回)技术,在学术界和工业界均已经得到了广泛的研究。与传统的基于字面匹配...
本文提出了一种无需相关标签的精确零射密集检索方法,通过使用假设文档嵌入和指令跟随语言模型,能够在没有相关标签的情况下构建有效的零射密集检索系统,并在各种任务和语言上取得了强大的性能。 背景信息: 论文背景: 密集检索是一种使用语义嵌入相似性来检索文档的方法,在Web搜索、问答和事实验证等任务中已经被证明是有...
GitHub - texttron/hyde: HyDE: Precise Zero-Shot Dense Retrieval without Relevance Labels 重要的图表 结果是生成的N个假设文档的Embedding均值 把查询的Embedding也算上求出均值 总结 在搜索引擎生命的初期,使用HyDE提供服务的性能可与微调模型相媲美,这是其他无需相关性的模型无法提供的。随着搜索日志的增长,可以...
随着预训练语言模型在自然语言处理领域的蓬勃发展,基于预训练语言模型的稠密检索(dense retrieval)近年来也变成了主流的一阶段检索(召回)技术,在学术界和工业界均已经得到了广泛的研究。与传统的基于字面匹配的稀疏检索(sparse retrieval)模型相比,稠密检索模型通过学习低维的查询和文档向量来实现语义级别的检索,能够更好...
论文标题:A Thorough Examination on Zero-shot Dense Retrieval 论文链接:https://arxiv.org/pdf/2204.12755.pdf 引言 随着预训练语言模型在自然语言处理领域的蓬勃发展,基于预训练语言模型的稠密检索(dense retrieval)近年来也变成了主流的一阶段检索(召回)技术,在学术界和工业界均已经得到了广泛的研究。与传统的基于...
Last commit date Latest commit History 7 Commits src/hyde .gitignore README.md approach.png hyde-demo.ipynb hyde-dl19.ipynb setup.py This is code repository for the paper:HyDE: Precise Zero-Shot Dense Retrieval without Relevance Labels. ...
We present a two-stage zero-shot linking algorithm, where each entity is defined only by a short textual description. The first stage does retrieval in a dense space defined by a bi-encoder that independently embeds the mention context and the entity descriptions. Each candidate is then re-...
In this section, we first formally define the problem of (zero-shot) dense retrieval. Then we will introduce how HyDE is designed to solve it. 在本节中,我们首先正式定义(零采样)密集检索的问题。然后我们将介绍如何设计HyDE来解决这个问题。
The dense retrieval model offers remarkable capabilities, yet it exhibits inconsistencies in the embedding space of queries and documents due to its dual-encoder structure. Addressing this limitation, we introduce Pseudo-query Embedding (PqE), a document expansion approach that eliminates the need for ...
Building effective dense retrieval systems remains difficult when relevance supervision is not available. Recent work has looked to overcome this challenge by using a Large Language Model (LLM) to generate hypothetical documents that can be used to find the closest real document. However, this ...