The rapid development of Large Language Models (LLMs) demonstrates remarkable
multilingual capabilities in natural language processing, attracting global attention in both …

Dense retrieval models using a transformer-based bi-encoder architecture have emerged as
an active area of research. In this article, we focus on the task of monolingual retrieval in a …

The principal goal of the TREC Neural Cross-Language Information Retrieval (NeuCLIR)
track is to study the impact of neural approaches to cross-language information retrieval. The …

In today's digital age, accessing information across language barriers poses a significant
challenge, with conventional search systems often struggling to interpret and retrieve …

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 …

In the contemporary digital landscape, search engines play an invaluable role in information
access, yet they often face challenges in Cross-Lingual Information Retrieval (CLIR) …

Large language models (LLMs) have shown impressive zero-shot capabilities in various
document reranking tasks. Despite their successful implementations, there is still a gap in …

Web resources are becoming more available in various languages, increasing the
importance of cross-lingual information retrieval (CLIR) in accessing information that is …

In this work, we reason how focusing on Information Retrieval (IR) for children and involving
them in participatory studies would benefit the IR community. The Child Computer …

In this paper, we introduce Neural Information Retrieval resources for 11 widely spoken
Indian Languages (Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi …