Document parsing is essential for converting unstructured and semi-structured documents-
such as contracts, academic papers, and invoices-into structured, machine-readable data …

Retrieval-augmented Generation (RAG) enhances Large Language Models (LLMs) by
integrating external knowledge to reduce hallucinations and incorporate up-to-date …

Generation with source attribution is important for enhancing the verifiability of retrieval-
augmented generation (RAG) systems. However, existing approaches in RAG primarily link …

Multimodal retrieval methods have limitations in handling complex, compositional queries
that require reasoning about the visual content of both the query and the retrieved entities …

Despite the rapidly growing demand for multimodal retrieval, progress in this field remains
severely constrained by a lack of training data. In this paper, we introduce MegaPairs, a …

Recent advancements in dense retrieval have introduced vision-language model (VLM)-
based retrievers, such as DSE and ColPali, which leverage document screenshots …

Large vision language models (LVLMs) have improved the document understanding
capabilities remarkably, enabling the handling of complex document elements, longer …

Retrieval-Augmented Generation (RAG) has demonstrated remarkable success in
enhancing Large Language Models (LLMs) through external knowledge integration, yet its …

The exploration of various vision-language tasks, such as visual captioning, visual question
answering, and visual commonsense reasoning, is an important area in artificial intelligence …

Archaeological catalogs, containing key elements such as artifact images, morphological
descriptions, and excavation information, are essential for studying artifact evolution and …