@inproceedings{wu-etal-2025-doc,
title = "Doc-React: Multi-page Heterogeneous Document Question-answering",
author = "Wu, Junda and
Xia, Yu and
Yu, Tong and
Chen, Xiang and
Harsha, Sai Sree and
Maharaj, Akash V and
Zhang, Ruiyi and
Bursztyn, Victor and
Kim, Sungchul and
Rossi, Ryan A. and
McAuley, Julian and
Li, Yunyao and
Sinha, Ritwik",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-short.6/",
doi = "10.18653/v1/2025.acl-short.6",
pages = "67--78",
ISBN = "979-8-89176-252-7",
abstract = "Answering questions over multi-page, multimodal documents, including text and figures, is a critical challenge for applications that require answers to integrate information across multiple modalities and contextual dependencies. Existing methods, such as single-turn retrieval-augmented generation (RAG), struggle to retrieve fine-grained and contextually relevant information from large, heterogeneous documents, leading to suboptimal performance. Inspired by iterative frameworks like ReAct, which refine retrieval through feedback, we propose Doc-React, an adaptive iterative framework that balances information gain and uncertainty reduction at each step. Doc-React leverages InfoNCE-guided retrieval to approximate mutual information, enabling dynamic sub-query generation and refinement. A large language model (LLM) serves as both a judge and generator, providing structured feedback to iteratively improve retrieval. By combining mutual information optimization with entropy-aware selection, Doc-React systematically captures relevant multimodal content, achieving strong performance on complex QA tasks"
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%0 Conference Proceedings
%T Doc-React: Multi-page Heterogeneous Document Question-answering
%A Wu, Junda
%A Xia, Yu
%A Yu, Tong
%A Chen, Xiang
%A Harsha, Sai Sree
%A Maharaj, Akash V.
%A Zhang, Ruiyi
%A Bursztyn, Victor
%A Kim, Sungchul
%A Rossi, Ryan A.
%A McAuley, Julian
%A Li, Yunyao
%A Sinha, Ritwik
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-252-7
%F wu-etal-2025-doc
%X Answering questions over multi-page, multimodal documents, including text and figures, is a critical challenge for applications that require answers to integrate information across multiple modalities and contextual dependencies. Existing methods, such as single-turn retrieval-augmented generation (RAG), struggle to retrieve fine-grained and contextually relevant information from large, heterogeneous documents, leading to suboptimal performance. Inspired by iterative frameworks like ReAct, which refine retrieval through feedback, we propose Doc-React, an adaptive iterative framework that balances information gain and uncertainty reduction at each step. Doc-React leverages InfoNCE-guided retrieval to approximate mutual information, enabling dynamic sub-query generation and refinement. A large language model (LLM) serves as both a judge and generator, providing structured feedback to iteratively improve retrieval. By combining mutual information optimization with entropy-aware selection, Doc-React systematically captures relevant multimodal content, achieving strong performance on complex QA tasks
%R 10.18653/v1/2025.acl-short.6
%U https://aclanthology.org/2025.acl-short.6/
%U https://doi.org/10.18653/v1/2025.acl-short.6
%P 67-78
Markdown (Informal)
[Doc-React: Multi-page Heterogeneous Document Question-answering](https://aclanthology.org/2025.acl-short.6/) (Wu et al., ACL 2025)
ACL
- Junda Wu, Yu Xia, Tong Yu, Xiang Chen, Sai Sree Harsha, Akash V Maharaj, Ruiyi Zhang, Victor Bursztyn, Sungchul Kim, Ryan A. Rossi, Julian McAuley, Yunyao Li, and Ritwik Sinha. 2025. Doc-React: Multi-page Heterogeneous Document Question-answering. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 67–78, Vienna, Austria. Association for Computational Linguistics.