Information retrieval: recent advances and beyond
This paper provides an extensive and thorough overview of the models and techniques
utilized in the first and second stages of the typical information retrieval processing chain …
utilized in the first and second stages of the typical information retrieval processing chain …
Beir: A heterogenous benchmark for zero-shot evaluation of information retrieval models
Existing neural information retrieval (IR) models have often been studied in homogeneous
and narrow settings, which has considerably limited insights into their out-of-distribution …
and narrow settings, which has considerably limited insights into their out-of-distribution …
Promptagator: Few-shot dense retrieval from 8 examples
Much recent research on information retrieval has focused on how to transfer from one task
(typically with abundant supervised data) to various other tasks where supervision is limited …
(typically with abundant supervised data) to various other tasks where supervision is limited …
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 …
[KİTAP][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 …
Improving the domain adaptation of retrieval augmented generation (RAG) models for open domain question answering
Abstract Retrieval Augment Generation (RAG) is a recent advancement in Open-Domain
Question Answering (ODQA). RAG has only been trained and explored with a Wikipedia …
Question Answering (ODQA). RAG has only been trained and explored with a Wikipedia …
GPL: Generative pseudo labeling for unsupervised domain adaptation of dense retrieval
Dense retrieval approaches can overcome the lexical gap and lead to significantly improved
search results. However, they require large amounts of training data which is not available …
search results. However, they require large amounts of training data which is not available …
Inpars: Unsupervised dataset generation for information retrieval
The Information Retrieval (IR) community has recently witnessed a revolution due to large
pretrained transformer models. Another key ingredient for this revolution was the MS …
pretrained transformer models. Another key ingredient for this revolution was the MS …
Pre-training methods in information retrieval
The core of information retrieval (IR) is to identify relevant information from large-scale
resources and return it as a ranked list to respond to user's information need. In recent years …
resources and return it as a ranked list to respond to user's information need. In recent years …
Inpars: Data augmentation for information retrieval using large language models
The information retrieval community has recently witnessed a revolution due to large
pretrained transformer models. Another key ingredient for this revolution was the MS …
pretrained transformer models. Another key ingredient for this revolution was the MS …