Yu-Cheng Chang

Multi-step retrieval-augmented generation has attracted increasing attention due to its capacity to improve the factuality of large language models with iterative retrieved knowledge. However, the performance of multi-step RAG systems is susceptible to potential retrieval noise and fabricated doc...

Retrieval-augmented generation (RAG) is indispensable for enhancing large language models. However, RAGs are increasingly susceptible to poisoning attacks, in which adversarial documents are injected to manipulate generator outputs. Previous methods rely on output-side signals such as perplexity...