Information Retrieval (IR) systems are crucial tools for users to access information, widely
applied in scenarios like search engines, question answering, and recommendation …

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

With the information explosion on the Web, search and recommendation are foundational
infrastructures to satisfying users' information needs. As the two sides of the same coin, both …

Multi-modal retrieval becomes increasingly popular in practice. However, the existing
retrievers are mostly text-oriented, which lack the capability to process visual information …

The rapid development of large language models (LLMs) has been witnessed in recent
years. Based on the powerful LLMs, multi-modal LLMs (MLLMs) extend the modality from …

Generative retrieval, which has demonstrated effectiveness in text-to-text retrieval, utilizes a
sequence-to-sequence model to directly generate candidate identifiers based on natural …

Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream
task execution by retrieving additional information, such as knowledge, skills, and tools from …

Multimodal Large Language Models (MLLMs) have attracted much attention for their
multifunctionality. However, traditional Transformer architectures incur significant overhead …

Multi-modal entity linking (MEL) is a challenging task that requires accurate prediction of
entities within extensive search spaces, utilizing multi-modal contexts. Existing generative …

The ability of AI to process vast datasets can enhance creativity, but its rigid knowledge base
and lack of reflective thinking limit sustainable design. Generative Design Thinking (GDT) …