Adversarial Question

Adversarial question research focuses on crafting questions designed to expose vulnerabilities in question-answering systems, particularly large language models (LLMs) and retrieval-augmented generation (RAG) systems. Current research emphasizes developing metrics to evaluate the effectiveness of adversarial questions, creating robust benchmarks, and exploring various attack strategies, including prompt leaking and entity substitution, to assess model robustness. This work is crucial for improving the reliability and safety of LLMs in real-world applications by identifying and mitigating their weaknesses, ultimately leading to more trustworthy and robust AI systems.