On the Limits of LLM Adaptability: Impact of Model-Internalized Priors on Annotation Task Performance

Large Language Models (LLMs) are increasingly used for zero-shot annotation and LLM-as-a-judge tasks, yet their reliability hinges on how model-internalized priors interact with user-provided instructions. We investigate three dimensions of this interaction: (1) how an LLM's familiarity with data and task definitions relates to performance, (2) whether additional information in prompts can correct zero-shot errors (``decision stickiness''), and (3) model susceptibility to misaligned task definitions. We introduce Definition-Specific Familiarity (DSF), which measures alignment between a model's elicited concept and the target definition. Across nine LLMs and five diverse toxicity datasets (spanning social media, gaming, news, and forums), DSF predicts model annotation performance after controlling for dataset identity (partial $r=+0.41$), and this association remains positive across all six prompting conditions tested. In contrast, three common text-memorization metrics show no positive association. We show that prompting has limited corrective power: only 34.8\% of zero-shot errors are corrected by additional instructions or examples, with high-confidence errors especially persistent. Misaligned definitions systematically shift predictions without reducing reported confidence, making confidence unreliable for detecting definition--policy mismatch. Together, these findings establish definition alignment as a practical model-selection criterion and show that better prompting alone cannot substitute for validating model--policy fit.