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 …

As a primary means of information acquisition, information retrieval (IR) systems, such as
search engines, have integrated themselves into our daily lives. These systems also serve …

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 …

One technique to improve the retrieval effectiveness of a search engine is to expand
documents with terms that are related or representative of the documents' content. From the …

Neural ranking models for information retrieval (IR) use shallow or deep neural networks to
rank search results in response to a query. Traditional learning to rank models employ …

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 …

Researchers have successfully applied large language models (LLMs) such as ChatGPT to
reranking in an information retrieval context, but to date, such work has mostly been built on …

We propose Generation-Augmented Retrieval (GAR) for answering open-domain questions,
which augments a query through text generation of heuristically discovered relevant …

This work tackles the perennial problem of reproducible baselines in information retrieval
research, focusing on bag-of-words ranking models. Although academic information …

Following recent successes in applying BERT to question answering, we explore simple
applications to ad hoc document retrieval. This required confronting the challenge posed by …