A Thorough Examination on Zero-shot Dense Retrieval 论文链接:arxiv.org/pdf/2204.1275 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的稀疏检索(spars...
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...
Zero-Shot (Dense) Retrieval的任务是由Thakur et al.(2021)为神经检索界定义的。他们的BEIR基准包含不同的检索任务。本文和许多后续研究通常考虑迁移学习设置,其中密集检索器首先使用多样化且富监督的语料库和查询集合进行学习,即MS-MARCO (Thakur et al., 2021;Wang et al., 2022;Yu等人,2022)。 However, as...
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...
在更改不同指令语言模型和使用微调编码器的情况下,所有模型都改进了无监督的Contriever,较大的模型带来了较大的改进。 使用微调编码器的HyDE对微调检索器的整体性能产生了负面影响,但性能下降仍然很小。 InstructGPT模型能够进一步提高性能,特别是在DL19上。
该工作提出了一种two-stage方法来完成zero-shot entity linking任务,主要基于fine-tuned BERT。 在第一个阶段,通过一个bi-encoder来检索所有与mention相关的实体;在第二个阶段,使用一个cross-encoder来对候选实体进行度量。注意,该工作核心为实体消歧,实体mention识别假定已经给出。(实际实现中使用Flair给出候选) ...
By zero-shot prompting large language models (LLMs), we generate a specific number of pseudo-queries for each document, which are used to mitigate inconsistencies in the embeddings between queries and documents. This innovative strategy employs a multi-stage retrieval process to expand documents, ...
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 ...
This is code repository for the paper:HyDE: Precise Zero-Shot Dense Retrieval without Relevance Labels. HyDEzero-shot instructs GPT3 to generate a fictional document and re-encodes it with unsupervised retriever Contriever to search in its embedding space. HyDE significantly outperforms Contriever ...
ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval 来自 arXiv.org 喜欢 0 阅读量: 3 作者:Y Yu,Y Zhuang,R Zhang,Y Meng,J Shen,C Zhang 摘要: With the development of large language models (LLMs), zero-shot learning has attracted much attention ...