Jun Zhao 0019, Can Zu, Xu Hao, Yi Lu, Wei He 0024, Yiwen Ding, Tao Gui, Qi Zhang 0001, Xuanjing Huang 0001. LONGAGENT: Achieving Question Answering for 128k-Token-Long Documents through Multi-Agent Collaboration. In Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen, editors, Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024, Miami, FL, USA, November 12-16, 2024. pages 16310-16324, Association for Computational Linguistics, 2024. [doi]
@inproceedings{0019ZHLHDGZ024,
title = {LONGAGENT: Achieving Question Answering for 128k-Token-Long Documents through Multi-Agent Collaboration},
author = {Jun Zhao 0019 and Can Zu and Xu Hao and Yi Lu and Wei He 0024 and Yiwen Ding and Tao Gui and Qi Zhang 0001 and Xuanjing Huang 0001},
year = {2024},
url = {https://aclanthology.org/2024.emnlp-main.912},
researchr = {https://researchr.org/publication/0019ZHLHDGZ024},
cites = {0},
citedby = {0},
pages = {16310-16324},
booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024, Miami, FL, USA, November 12-16, 2024},
editor = {Yaser Al-Onaizan and Mohit Bansal and Yun-Nung Chen},
publisher = {Association for Computational Linguistics},
isbn = {979-8-89176-164-3},
}