1 citations · 1 across the 6 of their papers we have counts for
10 papers
ReactBench: A Cause-Driven Benchmark for Multimodal Hallucination via Systematic Evaluation
Shizhe Zhou, Bohan Jia, Kai Wu +4
While multimodal large language models (MLLMs) have achieved rapid progress in vision-language understanding, they remain prone to multimodal hallucinations, producing responses th…
Who Taught the Lie? Responsibility Attribution for Poisoned Knowledge in Retrieval-Augmented Generation
Baolei Zhang, Haoran Xin, Yuxi Chen +6
Retrieval-Augmented Generation (RAG) integrates external knowledge into large language models to improve response quality. However, recent work has shown that RAG systems are highl…
Gradient Surgery for Safe LLM Fine-Tuning
Biao Yi, Jiahao Li, Baolei Zhang +4
Fine-tuning-as-a-Service introduces a critical vulnerability where a few malicious examples mixed into the user's fine-tuning dataset can compromise the safety alignment of Large L…
BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit
Biao Yi, Zekun Fei, Jianing Geng +4
Large reasoning models (LRMs) have emerged as a significant advancement in artificial intelligence, representing a specialized class of large language models (LLMs) designed to tac…
Probe before You Talk: Towards Black-box Defense against Backdoor Unalignment for Large Language Models
Biao Yi, Tiansheng Huang, Sishuo Chen +4
Backdoor unalignment attacks against Large Language Models (LLMs) enable the stealthy compromise of safety alignment using a hidden trigger while evading normal safety auditing. Th…
CTRAP: Embedding Collapse Trap to Safeguard Large Language Models from Harmful Fine-Tuning
Biao Yi, Tiansheng Huang, Baolei Zhang +4
Fine-tuning-as-a-service, while commercially successful for Large Language Model (LLM) providers, exposes models to harmful fine-tuning attacks. As a widely explored defense paradi…
Traceback of Poisoning Attacks to Retrieval-Augmented Generation
Baolei Zhang, Haoran Xin, Minghong Fang +4
Large language models (LLMs) integrated with retrieval-augmented generation (RAG) systems improve accuracy by leveraging external knowledge sources. However, recent research has re…
Practical Poisoning Attacks against Retrieval-Augmented Generation
Baolei Zhang, Yuxi Chen, Zhuqing Liu +4
Large language models (LLMs) have demonstrated impressive natural language processing abilities but face challenges such as hallucination and outdated knowledge. Retrieval-Augmente…
Prompt-Guided Internal States for Hallucination Detection of Large Language Models
Fujie Zhang, Peiqi Yu, Biao Yi +3
Large Language Models (LLMs) have demonstrated remarkable capabilities across a variety of tasks in different domains. However, they sometimes generate responses that are logically…
BadActs: A Universal Backdoor Defense in the Activation Space
Biao Yi, Sishuo Chen, Yiming Li +3
Backdoor attacks pose an increasingly severe security threat to Deep Neural Networks (DNNs) during their development stage. In response, backdoor sample purification has emerged as…