ComparisonQA: Evaluating Factuality Robustness of LLMs Through Knowledge Frequency Control and Uncertainty

Qing Zong, Zhaowei Wang, Tianshi Zheng, Xiyu Ren, Yangqiu Song


Abstract
The rapid development of LLMs has sparked extensive research into their factual knowledge. Current works find that LLMs fall short on questions around low-frequency entities. However, such proofs are unreliable since the questions can differ not only in entity frequency but also in difficulty themselves. So we introduce **ComparisonQA** benchmark, containing **283K** abstract questions, each instantiated by a pair of high-frequency and low-frequency entities. It ensures a controllable comparison to study the role of knowledge frequency in the performance of LLMs. Because the difference between such a pair is only the entity with different frequencies. In addition, we use both correctness and uncertainty to develop a two-round method to evaluate LLMs’ knowledge robustness. It aims to avoid possible semantic shortcuts which is a serious problem of current QA study. Experiments reveal that LLMs, including GPT-4o, exhibit particularly low robustness regarding low-frequency knowledge. Besides, we find that uncertainty can be used to effectively identify high-quality and shortcut-free questions while maintaining the data size. Based on this, we propose an automatic method to select such questions to form a subset called **ComparisonQA-Hard**, containing only hard low-frequency questions.
Anthology ID:
2025.findings-acl.212
Volume:
Findings of the Association for Computational Linguistics: ACL 2025
Month:
July
Year:
2025
Address:
Vienna, Austria
Editors:
Wanxiang Che, Joyce Nabende, Ekaterina Shutova, Mohammad Taher Pilehvar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4101–4117
Language:
URL:
https://preview.aclanthology.org/display_plenaries/2025.findings-acl.212/
DOI:
Bibkey:
Cite (ACL):
Qing Zong, Zhaowei Wang, Tianshi Zheng, Xiyu Ren, and Yangqiu Song. 2025. ComparisonQA: Evaluating Factuality Robustness of LLMs Through Knowledge Frequency Control and Uncertainty. In Findings of the Association for Computational Linguistics: ACL 2025, pages 4101–4117, Vienna, Austria. Association for Computational Linguistics.
Cite (Informal):
ComparisonQA: Evaluating Factuality Robustness of LLMs Through Knowledge Frequency Control and Uncertainty (Zong et al., Findings 2025)
Copy Citation:
PDF:
https://preview.aclanthology.org/display_plenaries/2025.findings-acl.212.pdf