@inproceedings{zheng-etal-2025-grada,
title = "{GRADA}: Graph-based Reranking against Adversarial Documents Attack",
author = "Zheng, Jingjie and
Gema, Aryo Pradipta and
Hong, Giwon and
He, Xuanli and
Minervini, Pasquale and
Sun, Youcheng and
Xu, Qiongkai",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1132/",
pages = "22255--22277",
ISBN = "979-8-89176-332-6",
abstract = "Retrieval Augmented Generation (RAG) frameworks can improve the factual accuracy of large language models (LLMs) by integrating external knowledge from retrieved documents, thereby overcoming the limitations of models' static intrinsic knowledge. However, these systems are susceptible to adversarial attacks that manipulate the retrieval process by introducing documents that are adversarial yet semantically similar to the query. Notably, while these adversarial documents resemble the query, they exhibit weak similarity to benign documents in the retrieval set. Thus, we propose a simple yet effective **G**raph-based **R**eranking against **A**dversarial **D**ocument **A**ttacks (GRADA) framework aiming at preserving retrieval quality while significantly reducing the success of adversaries. Our study evaluates the effectiveness of our approach through experiments conducted on six LLMs: GPT-3.5-Turbo, GPT-4o, Llama3.1-8b-Instruct, Llama3.1-70b-Instruct, Qwen2.5-7b-Instruct and Qwen2.5-14b-Instruct. We use three datasets to assess performance, with results from the Natural Questions dataset demonstrating up to an 80{\%} reduction in attack success rates while maintaining minimal loss in accuracy."
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<abstract>Retrieval Augmented Generation (RAG) frameworks can improve the factual accuracy of large language models (LLMs) by integrating external knowledge from retrieved documents, thereby overcoming the limitations of models’ static intrinsic knowledge. However, these systems are susceptible to adversarial attacks that manipulate the retrieval process by introducing documents that are adversarial yet semantically similar to the query. Notably, while these adversarial documents resemble the query, they exhibit weak similarity to benign documents in the retrieval set. Thus, we propose a simple yet effective **G**raph-based **R**eranking against **A**dversarial **D**ocument **A**ttacks (GRADA) framework aiming at preserving retrieval quality while significantly reducing the success of adversaries. Our study evaluates the effectiveness of our approach through experiments conducted on six LLMs: GPT-3.5-Turbo, GPT-4o, Llama3.1-8b-Instruct, Llama3.1-70b-Instruct, Qwen2.5-7b-Instruct and Qwen2.5-14b-Instruct. We use three datasets to assess performance, with results from the Natural Questions dataset demonstrating up to an 80% reduction in attack success rates while maintaining minimal loss in accuracy.</abstract>
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%0 Conference Proceedings
%T GRADA: Graph-based Reranking against Adversarial Documents Attack
%A Zheng, Jingjie
%A Gema, Aryo Pradipta
%A Hong, Giwon
%A He, Xuanli
%A Minervini, Pasquale
%A Sun, Youcheng
%A Xu, Qiongkai
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F zheng-etal-2025-grada
%X Retrieval Augmented Generation (RAG) frameworks can improve the factual accuracy of large language models (LLMs) by integrating external knowledge from retrieved documents, thereby overcoming the limitations of models’ static intrinsic knowledge. However, these systems are susceptible to adversarial attacks that manipulate the retrieval process by introducing documents that are adversarial yet semantically similar to the query. Notably, while these adversarial documents resemble the query, they exhibit weak similarity to benign documents in the retrieval set. Thus, we propose a simple yet effective **G**raph-based **R**eranking against **A**dversarial **D**ocument **A**ttacks (GRADA) framework aiming at preserving retrieval quality while significantly reducing the success of adversaries. Our study evaluates the effectiveness of our approach through experiments conducted on six LLMs: GPT-3.5-Turbo, GPT-4o, Llama3.1-8b-Instruct, Llama3.1-70b-Instruct, Qwen2.5-7b-Instruct and Qwen2.5-14b-Instruct. We use three datasets to assess performance, with results from the Natural Questions dataset demonstrating up to an 80% reduction in attack success rates while maintaining minimal loss in accuracy.
%U https://aclanthology.org/2025.emnlp-main.1132/
%P 22255-22277
Markdown (Informal)
[GRADA: Graph-based Reranking against Adversarial Documents Attack](https://aclanthology.org/2025.emnlp-main.1132/) (Zheng et al., EMNLP 2025)
ACL
- Jingjie Zheng, Aryo Pradipta Gema, Giwon Hong, Xuanli He, Pasquale Minervini, Youcheng Sun, and Qiongkai Xu. 2025. GRADA: Graph-based Reranking against Adversarial Documents Attack. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 22255–22277, Suzhou, China. Association for Computational Linguistics.