arXiv:2511.04694v5 Announce Type: replace Abstract: As large language model (LLM) based systems take on high-stakes roles in real-world decision-making, they must reconcile competing instructions from multiple sources within a single prompt context. Enforcing an instruction hierarchy, where higher-level directives override lower-priority requests, is critical to the reliability and control of LLMs. In this work, we reframe instruction hierarchy resolution as a reasoning task. The model must first "think" about the relationship between a given user prompt and higher-priority instructions before
The increasing deployment of LLMs in high-stakes environments necessitates greater control and reliability, making instruction hierarchy a critical area of research as models become more complex.
Reliable and controllable LLMs that can reconcile conflicting instructions are essential for their safe and effective integration into critical decision-making systems, impacting trust and adoption.
This research outlines a method to imbue LLMs with a reasoning capability for instruction hierarchies, moving beyond simple prompt parsing to more sophisticated command execution based on priorities.
- · LLM developers
- · AI safety researchers
- · Enterprises deploying AI in critical infrastructure
- · Users seeking more reliable AI assistants
- · Developers of uncontrolled or unpredictable LLMs
LLMs will become more predictable and less prone to misinterpreting conflicting user or system instructions.
This enhanced control could accelerate the deployment of LLMs into highly regulated industries where reliability is paramount.
Improved instruction following could lead to more complex and hierarchical AI agent systems capable of managing intricate, multi-layered tasks autonomously.
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Read at arXiv cs.CL