@inproceedings{ji-etal-2025-robguard,
title = "{R}o{BG}uard: Enhancing {LLM}s to Assess Risk of Bias in Clinical Trial Documents",
author = "Ji, Changkai and
Zhao, Bowen and
Wang, Zhuoyao and
Wang, Yingwen and
Zhang, Yuejie and
Cheng, Ying and
Feng, Rui and
Zhang, Xiaobo",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.84/",
pages = "1258--1277",
abstract = "Randomized Controlled Trials (RCTs) are rigorous clinical studies crucial for reliable decision-making, but their credibility can be compromised by bias. The Cochrane Risk of Bias tool (RoB 2) assesses this risk, yet manual assessments are time-consuming and labor-intensive. Previous approaches have employed Large Language Models (LLMs) to automate this process. However, they typically focus on manually crafted prompts and a restricted set of simple questions, limiting their accuracy and generalizability. Inspired by the human bias assessment process, we propose RoBGuard, a novel framework for enhancing LLMs to assess the risk of bias in RCTs. Specifically, RoBGuard integrates medical knowledge-enhanced question reformulation, multimodal document parsing, and multi-expert collaboration to ensure both completeness and accuracy. Additionally, to address the lack of suitable datasets, we introduce two new datasets: RoB-Item and RoB-Domain. Experimental results demonstrate RoBGuard`s effectiveness on the RoB-Item dataset, outperforming existing methods."
}
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<abstract>Randomized Controlled Trials (RCTs) are rigorous clinical studies crucial for reliable decision-making, but their credibility can be compromised by bias. The Cochrane Risk of Bias tool (RoB 2) assesses this risk, yet manual assessments are time-consuming and labor-intensive. Previous approaches have employed Large Language Models (LLMs) to automate this process. However, they typically focus on manually crafted prompts and a restricted set of simple questions, limiting their accuracy and generalizability. Inspired by the human bias assessment process, we propose RoBGuard, a novel framework for enhancing LLMs to assess the risk of bias in RCTs. Specifically, RoBGuard integrates medical knowledge-enhanced question reformulation, multimodal document parsing, and multi-expert collaboration to ensure both completeness and accuracy. Additionally, to address the lack of suitable datasets, we introduce two new datasets: RoB-Item and RoB-Domain. Experimental results demonstrate RoBGuard‘s effectiveness on the RoB-Item dataset, outperforming existing methods.</abstract>
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%0 Conference Proceedings
%T RoBGuard: Enhancing LLMs to Assess Risk of Bias in Clinical Trial Documents
%A Ji, Changkai
%A Zhao, Bowen
%A Wang, Zhuoyao
%A Wang, Yingwen
%A Zhang, Yuejie
%A Cheng, Ying
%A Feng, Rui
%A Zhang, Xiaobo
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F ji-etal-2025-robguard
%X Randomized Controlled Trials (RCTs) are rigorous clinical studies crucial for reliable decision-making, but their credibility can be compromised by bias. The Cochrane Risk of Bias tool (RoB 2) assesses this risk, yet manual assessments are time-consuming and labor-intensive. Previous approaches have employed Large Language Models (LLMs) to automate this process. However, they typically focus on manually crafted prompts and a restricted set of simple questions, limiting their accuracy and generalizability. Inspired by the human bias assessment process, we propose RoBGuard, a novel framework for enhancing LLMs to assess the risk of bias in RCTs. Specifically, RoBGuard integrates medical knowledge-enhanced question reformulation, multimodal document parsing, and multi-expert collaboration to ensure both completeness and accuracy. Additionally, to address the lack of suitable datasets, we introduce two new datasets: RoB-Item and RoB-Domain. Experimental results demonstrate RoBGuard‘s effectiveness on the RoB-Item dataset, outperforming existing methods.
%U https://aclanthology.org/2025.coling-main.84/
%P 1258-1277
Markdown (Informal)
[RoBGuard: Enhancing LLMs to Assess Risk of Bias in Clinical Trial Documents](https://aclanthology.org/2025.coling-main.84/) (Ji et al., COLING 2025)
ACL
- Changkai Ji, Bowen Zhao, Zhuoyao Wang, Yingwen Wang, Yuejie Zhang, Ying Cheng, Rui Feng, and Xiaobo Zhang. 2025. RoBGuard: Enhancing LLMs to Assess Risk of Bias in Clinical Trial Documents. In Proceedings of the 31st International Conference on Computational Linguistics, pages 1258–1277, Abu Dhabi, UAE. Association for Computational Linguistics.