Papers by Yu-An Liu
The Silent Saboteur: Imperceptible Adversarial Attacks against Black-Box Retrieval-Augmented Generation Systems (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing studies have focused on corpus poisoning, but there are no studies on adversarial attacks on RAG systems. |
| Approach: | They propose a novel imperceptible retrieve-to-generate attack against RAG systems . they propose regenerative reinforcement learning framework that tracks interactions between attacker and target RAG . |
| Outcome: | The proposed framework outperforms existing attacks on factual and non-factual RAG systems with small imperceptible text perturbations. |
Compete to Complete: Co-opetition Adversarial Learning for Retrieval-Augmented Generation (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to reduce hallucination in large language models lack a robust mechanism for generating a generative model. |
| Approach: | They propose a framework that formulates retriever–generator training in RAG as a minimax game. |
| Outcome: | The proposed framework improves retrieval-augmented generation performance on seven benchmark datasets. |
A Generative Framework for Personalized Sticker Retrieval (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing relevance-based generative retrieval methods lack personalization, leading to a mismatch between diverse user expectations and the retrieved results. |
| Approach: | They propose a representation learning model that learns discriminative user representations to encode user-specific sticker preferences. |
| Outcome: | The proposed framework outperforms state-of-the-art methods in generating relevant stickers for queries. |
Stop Hardening Everything: A Training-Free Neuron-Level Defense for Neural Ranking Models (2026.acl-long)
Copied to clipboard
| Challenge: | Existing defenses for neural ranking models are data-centric and require retraining and adversarial data generation. |
| Approach: | They propose a model-centric defense that addresses vulnerability at its architectural source without costly retraining or adversarial data generation. |
| Outcome: | The proposed approach outperforms state-of-the-art models on MS MARCO and TREC 19 while maintaining strong performance on clean data. |