May 14, 2024, 4:50 a.m. | Haoran Li, Yulin Chen, Zihao Zheng, Qi Hu, Chunkit Chan, Heshan Liu, Yangqiu Song

cs.CL updates on

arXiv:2405.07667v1 Announce Type: cross
Abstract: With rapid advances, generative large language models (LLMs) dominate various Natural Language Processing (NLP) tasks from understanding to reasoning. Yet, language models' inherent vulnerabilities may be exacerbated due to increased accessibility and unrestricted model training on massive textual data from the Internet. A malicious adversary may publish poisoned data online and conduct backdoor attacks on the victim LLMs pre-trained on the poisoned data. Backdoored LLMs behave innocuously for normal queries and generate harmful responses when …

abstract accessibility advances arxiv backdoor data generative internet language language models language processing large language large language models llms massive natural natural language natural language processing nlp processing reasoning tasks textual training type understanding vulnerabilities

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