Instruction Hierarchy

Instruction hierarchy research focuses on improving how large language models (LLMs) interpret and prioritize multiple instructions, especially in complex or conflicting scenarios. Current work investigates methods for creating and utilizing datasets with fine-grained instruction variations, developing efficient inference techniques for large vision-language models, and designing architectures that allow LLMs to prioritize instructions based on their source or importance (e.g., system prompts versus user input). This research is crucial for enhancing the robustness and safety of LLMs, mitigating vulnerabilities like prompt injection attacks, and improving their performance in real-world applications requiring nuanced instruction following.