Papers with DocQA
MC-indexing: Effective Long Document Retrieval via Multi-view Content-aware Indexing (2024.findings-emnlp)
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| Challenge: | Existing methods for document question answering do not consider content structures, resulting chunks exclude vital information or include irrelevant content. |
| Approach: | They propose a method that segments document into content chunks and represents each content chunk in raw-text, keywords, and summary views. |
| Outcome: | The proposed method significantly improves recall of long document question answering datasets compared to state-of-the-art chunking schemes. |
RASD: Retrieval-Augmented Speculative Decoding (2025.findings-acl)
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| Challenge: | Existing methods for generating draft tokens rely on lightweight draft models or additional model structures to generate tokens and retrieve context from databases. |
| Approach: | They propose to use a pruning method to enhance model-based speculative decoding by combining the best-fit model with the best retrieval tree. |
| Outcome: | The proposed method achieves state-of-the-art inference acceleration across tasks such as DocQA, Summary, Code, and In-Domain QA. |
MoLoRAG: Bootstrapping Document Understanding via Multi-modal Logic-aware Retrieval (2025.emnlp-main)
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| Challenge: | Document Understanding is a foundational AI capability with broad applications . Large Vision-Language Models (LLMs) can't handle multi-page document comprehension . a logic-aware retrieval framework for multi-modal, multi- page document understanding is proposed . |
| Approach: | They propose a logic-aware retrieval framework for multi-modal, multi-page document understanding . MoLoRAG uses semantic and logical relevance to deliver more accurate retrieval . |
| Outcome: | The proposed framework improves on four DocQA datasets and demonstrates 9.68% accuracy improvement over existing methods. |