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 …

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 …

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 …

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 …

Popularized by the Differentiable Search Index, the emerging paradigm of generative
retrieval re-frames the classic information retrieval problem into a sequence-to-sequence …

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

Zero-shot text rankers powered by recent LLMs achieve remarkable ranking performance by
simply prompting. Existing prompts for pointwise LLM rankers mostly ask the model to …

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 …

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