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 …
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 …
Optimization methods for personalizing large language models through retrieval augmentation
This paper studies retrieval-augmented approaches for personalizing large language
models (LLMs), which potentially have a substantial impact on various applications and …
models (LLMs), which potentially have a substantial impact on various applications and …
Pseudo relevance feedback with deep language models and dense retrievers: Successes and pitfalls
Pseudo Relevance Feedback (PRF) is known to improve the effectiveness of bag-of-words
retrievers. At the same time, deep language models have been shown to outperform …
retrievers. At the same time, deep language models have been shown to outperform …
Neural disentanglement of query difficulty and semantics
Researchers have shown that the retrieval effectiveness of queries may depend on other
factors in addition to the semantics of the query. In other words, several queries expressed …
factors in addition to the semantics of the query. In other words, several queries expressed …
[HTML][HTML] A self-supervised language model selection strategy for biomedical question answering
Large neural-based Pre-trained Language Models (PLM) have recently gained much
attention due to their noteworthy performance in many downstream Information Retrieval …
attention due to their noteworthy performance in many downstream Information Retrieval …
Quantifying ranker coverage of different query subspaces
The information retrieval community has observed significant performance improvements
over various tasks due to the introduction of neural architectures. However, such …
over various tasks due to the introduction of neural architectures. However, such …
[HTML][HTML] Improving zero-shot retrieval using dense external expansion
Pseudo-relevance feedback (PRF) is a classical technique to improve search engine
retrieval effectiveness, by closing the vocabulary gap between users' query formulations and …
retrieval effectiveness, by closing the vocabulary gap between users' query formulations and …
Generative information retrieval evaluation
In this chapter, we consider generative information retrieval (IR) evaluation from two distinct
but interrelated perspectives. First, Large Language Models (LLMs) themselves are rapidly …
but interrelated perspectives. First, Large Language Models (LLMs) themselves are rapidly …
To interpolate or not to interpolate: Prf, dense and sparse retrievers
Current pre-trained language model approaches to information retrieval can be broadly
divided into two categories: sparse retrievers (to which belong also non-neural approaches …
divided into two categories: sparse retrievers (to which belong also non-neural approaches …