arXiv:2605.20382v1 Announce Type: new Abstract: Language models are trained to follow instructions, but they are also powerful pattern completers. What happens when these two objectives conflict? We construct conversations in which a user instruction to behave in a target way T (e.g., always output a specific token, answer in a particular language, or adopt a persona) is opposed by N hardcoded assistant turns demonstrating a competing pattern P. We then measure instruction-following (IF) rates in this setting, across 13 models and 16 different instructions, for up to 50 turns. Average instruct
The proliferation of advanced LLMs and their integration into various applications makes understanding their internal mechanisms and potential failure modes critical for their safe and effective deployment.
This research highlights a fundamental tension within LLMs between explicit instructions and learned patterns, which has significant implications for reliability, safety, and alignment of AI systems.
Our understanding of LLM control mechanisms is deepened, revealing that simply providing instructions may not be sufficient to override deeply embedded learned behaviors, impacting design principles for future models.
- · AI Safety Researchers
- · Developers of robust LLM fine-tuning methods
- · Companies investing in explainable AI
- · Developers relying solely on prompt engineering for complex behavior control
- · Users expecting perfect instruction following
- · Companies deploying unverified LLM agents
Further research and development will be directed towards mitigating instruction-induction conflicts in LLMs.
New techniques for 'unlearning' or overriding undesirable patterns in LLMs will emerge, potentially changing model training paradigms.
The development of highly reliable AI agents will accelerate, as their foundational models become more predictable in following explicit commands.
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