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

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 …

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 …

Doc2Query—the process of expanding the content of a document before indexing using a
sequence-to-sequence model—has emerged as a prominent technique for improving the …

The MS MARCO-passage dataset has been the main large-scale dataset open to the IR
community and it has fostered successfully the development of novel neural retrieval models …

The advent of multilingual language models has generated a resurgence of interest in cross-
lingual information retrieval (CLIR), which is the task of searching documents in one …

Listwise rerankers based on large language models (LLM) are the zero-shot state-of-the-art.
However, current works in this direction all depend on the GPT models, making it a single …

Neural Information Retrieval has advanced rapidly in high-resource languages, but progress
in lower-resource ones such as Japanese has been hindered by data scarcity, among other …

Neural networks-especially those that use large, pre-trained language models-have
improved search engines in various ways. Most prominently, they can estimate the …

Cross-encoders distilled from large language models are more effective re-rankers than
cross-encoders fine-tuned using manually labeled data. However, the distilled models do …