Semantic models for the first-stage retrieval: A comprehensive review
Multi-stage ranking pipelines have been a practical solution in modern search systems,
where the first-stage retrieval is to return a subset of candidate documents and latter stages …
where the first-stage retrieval is to return a subset of candidate documents and latter stages …
Colbertv2: Effective and efficient retrieval via lightweight late interaction
Neural information retrieval (IR) has greatly advanced search and other knowledge-
intensive language tasks. While many neural IR methods encode queries and documents …
intensive language tasks. While many neural IR methods encode queries and documents …
Autoregressive search engines: Generating substrings as document identifiers
Abstract Knowledge-intensive language tasks require NLP systems to both provide the
correct answer and retrieve supporting evidence for it in a given corpus. Autoregressive …
correct answer and retrieve supporting evidence for it in a given corpus. Autoregressive …
Dense text retrieval based on pretrained language models: A survey
Text retrieval is a long-standing research topic on information seeking, where a system is
required to return relevant information resources to user's queries in natural language. From …
required to return relevant information resources to user's queries in natural language. From …
[图书][B] Pretrained transformers for text ranking: Bert and beyond
The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in
response to a query. Although the most common formulation of text ranking is search …
response to a query. Although the most common formulation of text ranking is search …
From distillation to hard negative sampling: Making sparse neural ir models more effective
Neural retrievers based on dense representations combined with Approximate Nearest
Neighbors search have recently received a lot of attention, owing their success to distillation …
Neighbors search have recently received a lot of attention, owing their success to distillation …
Query performance prediction for neural IR: Are we there yet?
Abstract Evaluation in Information Retrieval (IR) relies on post-hoc empirical procedures,
which are time-consuming and expensive operations. To alleviate this, Query Performance …
which are time-consuming and expensive operations. To alleviate this, Query Performance …
Query2doc: Query expansion with large language models
This paper introduces a simple yet effective query expansion approach, denoted as
query2doc, to improve both sparse and dense retrieval systems. The proposed method first …
query2doc, to improve both sparse and dense retrieval systems. The proposed method first …
SPLADE v2: Sparse lexical and expansion model for information retrieval
In neural Information Retrieval (IR), ongoing research is directed towards improving the first
retriever in ranking pipelines. Learning dense embeddings to conduct retrieval using …
retriever in ranking pipelines. Learning dense embeddings to conduct retrieval using …
[HTML][HTML] Miracl: A multilingual retrieval dataset covering 18 diverse languages
MIRACL is a multilingual dataset for ad hoc retrieval across 18 languages that collectively
encompass over three billion native speakers around the world. This resource is designed to …
encompass over three billion native speakers around the world. This resource is designed to …