Multi Document

Multi-document processing focuses on effectively extracting information and generating coherent outputs from collections of related documents, addressing challenges like information redundancy, contradiction, and the lack of a unified narrative. Current research emphasizes developing models that can handle diverse document types and modalities (text, images), often employing transformer-based architectures like perceivers and leveraging techniques such as retrieval-augmented generation (RAG) and hierarchical encoding-decoding. This field is crucial for advancing applications like fact-checking, summarization, and question answering, improving information access and analysis across various domains.