Large language models (LLMs) demonstrate powerful capabilities, but they still face
challenges in practical applications, such as hallucinations, slow knowledge updates, and …

Large language models (LLMs) have significantly advanced the field of natural language
processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of …

Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

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 …

This paper presents E5, a family of state-of-the-art text embeddings that transfer well to a
wide range of tasks. The model is trained in a contrastive manner with weak supervision …

Ranking documents using Large Language Models (LLMs) by directly feeding the query and
candidate documents into the prompt is an interesting and practical problem. However …

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 …

In this paper, we introduce a novel and simple method for obtaining high-quality text
embeddings using only synthetic data and less than 1k training steps. Unlike existing …

Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of
large language models (LLMs) by incorporating external knowledge sources. This method …

We study the problem of retrieval with instructions, where users of a retrieval system
explicitly describe their intent along with their queries. We aim to develop a general-purpose …