Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

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 …

Pre-trained models have achieved state-of-the-art results in various Natural Language
Processing (NLP) tasks. Recent works such as T5 and GPT-3 have shown that scaling up …

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 …

In open-domain question answering, dense passage retrieval has become a new paradigm
to retrieve relevant passages for finding answers. Typically, the dual-encoder architecture is …

Conducting text retrieval in a dense learned representation space has many intriguing
advantages over sparse retrieval. Yet the effectiveness of dense retrieval (DR) often requires …

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 …

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 …

Recent progress in Natural Language Understanding (NLU) is driving fast-paced advances
in Information Retrieval (IR), largely owed to fine-tuning deep language models (LMs) for …

With the capability of modeling bidirectional contexts, denoising autoencoding based
pretraining like BERT achieves better performance than pretraining approaches based on …