@inproceedings{hu-etal-2024-evaluating,
title = "Evaluating Robustness of Generative Search Engine on Adversarial Factoid Questions",
author = "Hu, Xuming and
Li, Xiaochuan and
Chen, Junzhe and
Li, Yinghui and
Li, Yangning and
Li, Xiaoguang and
Wang, Yasheng and
Liu, Qun and
Wen, Lijie and
Yu, Philip and
Guo, Zhijiang",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.633",
pages = "10650--10671",
abstract = "Generative search engines have the potential to transform how people seek information online, but generated responses from existing large language models (LLMs)-backed generative search engines may not always be accurate. Nonetheless, retrieval-augmented generation exacerbates safety concerns, since adversaries may successfully evade the entire system by subtly manipulating the most vulnerable part of a claim. To this end, we propose evaluating the robustness of generative search engines in the realistic and high-risk setting, where adversaries have only black-box system access and seek to deceive the model into returning incorrect responses. Through a comprehensive human evaluation of various generative search engines, such as Bing Chat, PerplexityAI, and YouChat across diverse queries, we demonstrate the effectiveness of adversarial factual questions in inducing incorrect responses. Moreover, retrieval-augmented generation exhibits a higher susceptibility to factual errors compared to LLMs without retrieval. These findings highlight the potential security risks of these systems and emphasize the need for rigorous evaluation before deployment. The dataset and code will be publicly available.",
}
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<abstract>Generative search engines have the potential to transform how people seek information online, but generated responses from existing large language models (LLMs)-backed generative search engines may not always be accurate. Nonetheless, retrieval-augmented generation exacerbates safety concerns, since adversaries may successfully evade the entire system by subtly manipulating the most vulnerable part of a claim. To this end, we propose evaluating the robustness of generative search engines in the realistic and high-risk setting, where adversaries have only black-box system access and seek to deceive the model into returning incorrect responses. Through a comprehensive human evaluation of various generative search engines, such as Bing Chat, PerplexityAI, and YouChat across diverse queries, we demonstrate the effectiveness of adversarial factual questions in inducing incorrect responses. Moreover, retrieval-augmented generation exhibits a higher susceptibility to factual errors compared to LLMs without retrieval. These findings highlight the potential security risks of these systems and emphasize the need for rigorous evaluation before deployment. The dataset and code will be publicly available.</abstract>
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%0 Conference Proceedings
%T Evaluating Robustness of Generative Search Engine on Adversarial Factoid Questions
%A Hu, Xuming
%A Li, Xiaochuan
%A Chen, Junzhe
%A Li, Yinghui
%A Li, Yangning
%A Li, Xiaoguang
%A Wang, Yasheng
%A Liu, Qun
%A Wen, Lijie
%A Yu, Philip
%A Guo, Zhijiang
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F hu-etal-2024-evaluating
%X Generative search engines have the potential to transform how people seek information online, but generated responses from existing large language models (LLMs)-backed generative search engines may not always be accurate. Nonetheless, retrieval-augmented generation exacerbates safety concerns, since adversaries may successfully evade the entire system by subtly manipulating the most vulnerable part of a claim. To this end, we propose evaluating the robustness of generative search engines in the realistic and high-risk setting, where adversaries have only black-box system access and seek to deceive the model into returning incorrect responses. Through a comprehensive human evaluation of various generative search engines, such as Bing Chat, PerplexityAI, and YouChat across diverse queries, we demonstrate the effectiveness of adversarial factual questions in inducing incorrect responses. Moreover, retrieval-augmented generation exhibits a higher susceptibility to factual errors compared to LLMs without retrieval. These findings highlight the potential security risks of these systems and emphasize the need for rigorous evaluation before deployment. The dataset and code will be publicly available.
%U https://aclanthology.org/2024.findings-acl.633
%P 10650-10671
Markdown (Informal)
[Evaluating Robustness of Generative Search Engine on Adversarial Factoid Questions](https://aclanthology.org/2024.findings-acl.633) (Hu et al., Findings 2024)
ACL
- Xuming Hu, Xiaochuan Li, Junzhe Chen, Yinghui Li, Yangning Li, Xiaoguang Li, Yasheng Wang, Qun Liu, Lijie Wen, Philip Yu, and Zhijiang Guo. 2024. Evaluating Robustness of Generative Search Engine on Adversarial Factoid Questions. In Findings of the Association for Computational Linguistics ACL 2024, pages 10650–10671, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.