Papers with Document question answering
EXPLAIN: Enhancing Retrieval-Augmented Generation with Entity Summary (2025.acl-industry)
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Yaozhen Liang, Xiao Liu, Jiajun Yu, Zhouhua Fang, Qunsheng Zou, Linghan Zheng, Yong Li, Zhiwei Liu, Haishuai Wang
| Challenge: | Existing document question answering methods reduce inference costs and input tokens. |
| Approach: | They propose a retrieval-augmented generation method that automatically extracts useful entities and generates summaries from documents. |
| Outcome: | The proposed method surpasses baseline retrieval-augmented generation (RAG) and long-context question answering (LC) methods achieve higher accuracy by processing entire documents, but at the cost of increased computational Corresponding authors. |
JDocQA: Japanese Document Question Answering Dataset for Generative Language Models (2024.lrec-main)
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| Challenge: | Document question answering is a task of question answering on given documents such as reports, slides, pamphlets, and websites. |
| Approach: | They propose a large-scale document-based QA dataset that requires both visual and textual information to answer questions. |
| Outcome: | The proposed dataset incorporates multiple categories of questions and unanswerable questions from the document for realistic question-answering applications. |