@inproceedings{jing-etal-2024-autorg,
title = "{A}uto{RG}:一种大小模型协同的自动报告生成框架({A}uto{RG}: An automatic report generation framework for Large and small model collaboration)",
author = "Jing, Zhang and
Jiangming, Shu and
Yuxiang, Zhang and
Bin, Wu and
Wei, Wang and
Jian, Yu and
Jitao, Sang",
editor = "Sun, Maosong and
Liang, Jiye and
Han, Xianpei and
Liu, Zhiyuan and
He, Yulan",
booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)",
month = jul,
year = "2024",
address = "Taiyuan, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2024.ccl-1.42/",
pages = "539--552",
language = "zho",
abstract = "{\textquotedblleft}自动报告生成技术在提高工作效率和节约人力资源方面具有显著潜力。大语言模型的出现使得报告流畅度与可解释性得到提升。然而,现有工作仍依赖人工,缺乏灵活性和丰富度。同时,小模型错误或冗余的输出与大模型自身的随机性会导致报告质量不稳定。本文提出大小模型协同的自动报告生成框架AutoRG,通过大模型的工具理解与规划能力减少人工干预,提升报告丰富度,并通过信息修正与报告迭代机制提高报告的稳定性。本文以自动专利报告生成为场景,从多个维度对AutoRG进行全面测试。结果表明,该框架在提高报告生成的丰富度和质量稳定性方面具有显著优势。{\textquotedblright}"
}
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<abstract>“自动报告生成技术在提高工作效率和节约人力资源方面具有显著潜力。大语言模型的出现使得报告流畅度与可解释性得到提升。然而,现有工作仍依赖人工,缺乏灵活性和丰富度。同时,小模型错误或冗余的输出与大模型自身的随机性会导致报告质量不稳定。本文提出大小模型协同的自动报告生成框架AutoRG,通过大模型的工具理解与规划能力减少人工干预,提升报告丰富度,并通过信息修正与报告迭代机制提高报告的稳定性。本文以自动专利报告生成为场景,从多个维度对AutoRG进行全面测试。结果表明,该框架在提高报告生成的丰富度和质量稳定性方面具有显著优势。”</abstract>
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%0 Conference Proceedings
%T AutoRG:一种大小模型协同的自动报告生成框架(AutoRG: An automatic report generation framework for Large and small model collaboration)
%A Jing, Zhang
%A Jiangming, Shu
%A Yuxiang, Zhang
%A Bin, Wu
%A Wei, Wang
%A Jian, Yu
%A Jitao, Sang
%Y Sun, Maosong
%Y Liang, Jiye
%Y Han, Xianpei
%Y Liu, Zhiyuan
%Y He, Yulan
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G zho
%F jing-etal-2024-autorg
%X “自动报告生成技术在提高工作效率和节约人力资源方面具有显著潜力。大语言模型的出现使得报告流畅度与可解释性得到提升。然而,现有工作仍依赖人工,缺乏灵活性和丰富度。同时,小模型错误或冗余的输出与大模型自身的随机性会导致报告质量不稳定。本文提出大小模型协同的自动报告生成框架AutoRG,通过大模型的工具理解与规划能力减少人工干预,提升报告丰富度,并通过信息修正与报告迭代机制提高报告的稳定性。本文以自动专利报告生成为场景,从多个维度对AutoRG进行全面测试。结果表明,该框架在提高报告生成的丰富度和质量稳定性方面具有显著优势。”
%U https://aclanthology.org/2024.ccl-1.42/
%P 539-552
Markdown (Informal)
[AutoRG:一种大小模型协同的自动报告生成框架(AutoRG: An automatic report generation framework for Large and small model collaboration)](https://aclanthology.org/2024.ccl-1.42/) (Jing et al., CCL 2024)
ACL