HiQA: A Hierarchical Contextual Augmentation RAG for Multi-Documents QA
This addresses a practical challenge in QA systems for users dealing with large, similar document sets, though it appears incremental as it builds on existing RAG methods.
The paper tackles the problem of limited retrieval accuracy in retrieval-augmented generation (RAG) for multi-document question-answering (MDQA) when faced with numerous indistinguishable documents, and presents HiQA, a framework that integrates cascading metadata and multi-route retrieval to achieve state-of-the-art performance in multi-document environments.
Retrieval-augmented generation (RAG) has rapidly advanced the language model field, particularly in question-answering (QA) systems. By integrating external documents during the response generation phase, RAG significantly enhances the accuracy and reliability of language models. This method elevates the quality of responses and reduces the frequency of hallucinations, where the model generates incorrect or misleading information. However, these methods exhibit limited retrieval accuracy when faced with numerous indistinguishable documents, presenting notable challenges in their practical application. In response to these emerging challenges, we present HiQA, an advanced multi-document question-answering (MDQA) framework that integrates cascading metadata into content and 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.