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 …

Recently, tool learning with large language models (LLMs) has emerged as a promising
paradigm for augmenting the capabilities of LLMs to tackle highly complex problems …

In this paper, we present a new embedding model, called M3-Embedding, which is
distinguished for its versatility in Multi-Linguality, Multi-Functionality, and Multi-Granularity. It …

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 …

Text embeddings are useful features in many applications such as semantic search and
computing text similarity. Previous work typically trains models customized for different use …

Large Language Models (LLMs) are capable of performing zero-shot closed-book question
answering tasks, based on their internal knowledge stored in parameters during pre …

Neural information retrieval (IR) has greatly advanced search and other knowledge-
intensive language tasks. While many neural IR methods encode queries and documents …

It has been shown that dual encoders trained on one domain often fail to generalize to other
domains for retrieval tasks. One widespread belief is that the bottleneck layer of a dual …

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 …

Motivation Information retrieval (IR) is essential in biomedical knowledge acquisition and
clinical decision support. While recent progress has shown that language model encoders …