Do as I Say, Not as I Do: Instruction-Induction Conflict in LLMs

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—such as always outputting a specific token, answering in a particular language, or adopting a persona—is opposed by N hardcoded assistant turns demonstrating a competing pattern P, and measure instruction-following (IF) rates across 13 models and 16 different instructions as N increases. Average instruction-following rates range from 1% to 99% across models, and are largely uncorrelated with standard capability benchmarks. The gradual transition from instruction-following to pattern-following is universal but highly model-dependent. Robustness is shaped by two factors: models follow an instruction longer when its content aligns with their trained value priors, and especially when it requires long, varied responses rather than a single repeated token, an effect we trace to the length and diversity of the outputs themselves rather than to engagement with the question. Chain-of-thought reasoning raises instruction-following but does not eliminate susceptibility, and can open a dissociation between the model's deliberation and its output. Across models of widely varying capability, instruction-following thus remains sensitive to mounting in-context pressure.