HiQA: A Hierarchical Contextual Augmentation RAG for Massive Documents
  QA
Xinyue Chen Pengyu Gao Jiangjiang Song Xiaoyang Tan
As language model agents leveraging external tools rapidly evolve,
significant progress has been made in question-answering(QA) methodologies
utilizing supplementary documents and the Retrieval-Augmented Generation (RAG)
approach. This advancement has improved the response quality of language models
and alleviates the appearance of hallucination. However, these methods exhibit
limited retrieval accuracy when faced with massive indistinguishable documents,
presenting notable challenges in their practical application. In response to
these emerging challenges, we present HiQA, an advanced framework for
multi-document question-answering (MDQA) that integrates cascading metadata
into content as well as a multi-route retrieval mechanism. We also release a
benchmark called MasQA to evaluate and research in MDQA. Finally, HiQA
demonstrates the state-of-the-art performance in multi-document environments.