Instructional Segment Embedding: Improving LLM Safety with Instruction Hierarchy
Tong Wu Shujian Zhang Kaiqiang Song Silei Xu Sanqiang Zhao Ravi Agrawal Sathish Reddy Indurthi Chong Xiang Prateek Mittal Wenxuan Zhou
Large Language Models (LLMs) are susceptible to security and safety threats, such as prompt injection, prompt extraction, and harmful requests.
One major cause of these vulnerabilities is the lack of an instruction hierarchy.
Modern LLM architectures treat all inputs equally, failing to distinguish between and prioritize various types of instructions, such as system messages, user prompts, and data. 
As a result, lower-priority user prompts may override more critical system instructions, including safety protocols. 
Existing approaches to achieving instruction hierarchy, such as delimiters and instruction-based training, do not address this issue at the architectural level.
We introduce the $\textbf{I}$nstructional $\textbf{S}$egment $\textbf{E}$mbedding (ISE) technique, inspired by BERT, to modern large language models, which embeds instruction priority information directly into the model. 
This approach enables models to explicitly differentiate and prioritize various instruction types, significantly improving safety against malicious prompts that attempt to override priority rules. 
Our experiments on the Structured Query and Instruction Hierarchy benchmarks demonstrate an average robust accuracy increase of up to 15.75\% and 18.68\%, respectively. 
Furthermore, we observe an improvement in the instruction-following capability of up to 4.1\% on AlpacaEval. 
Overall, our approach offers a promising direction for enhancing the safety and effectiveness of LLM architectures.
Instruction Hierarchy, Segment Embedding, LLM safety and robustness