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

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 …

As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can
offer reliable and up-to-date external knowledge, providing huge convenience for numerous …

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 …

This paper introduces a simple yet effective query expansion approach, denoted as
query2doc, to improve both sparse and dense retrieval systems. The proposed method first …

Large language models (LLMs) augmented with external data have demonstrated
remarkable capabilities in completing real-world tasks. Techniques for integrating external …

Large language models are powerful text processors and reasoners, but are still subject to
limitations including outdated knowledge and hallucinations, which necessitates connecting …

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

Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating
up-to-date information, mitigating hallucinations, and enhancing response quality …