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 …

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 …

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 …

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 …

Retrieval-Augmented Generation (RAG) merges retrieval methods with deep learning
advancements to address the static limitations of large language models (LLMs) by enabling …

In information retrieval, proprietary large language models (LLMs) such as GPT-4 and open-
source counterparts such as LLaMA and Vicuna have played a vital role in reranking …

Recently, InPars introduced a method to efficiently use large language models (LLMs) in
information retrieval tasks: via few-shot examples, an LLM is induced to generate relevant …

While large language models (LLMs) have shown impressive NLP capabilities, existing IR
applications mainly focus on prompting LLMs to generate query expansions or generating …

Large Language Models (LLMs) have significantly enhanced Information Retrieval (IR)
across various modules, such as reranking. Despite impressive performance, current zero …

Large language models (LLMs) exhibit positional bias in how they use context, which
especially complicates listwise ranking. To address this, we propose permutation self …