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 affects performance, (2) the extent to which additional information in prompts can correct zero-shot errors ("decision stickiness"), and (3) model susceptibility to misaligned task

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ethics.ai (30 May 2026), “On the Limits of LLM Adaptability: Impact of Model-Internalized Priors on Annotation Task Performance,” evidence record 3406, https://ethics.ai/record/3406 (originally published by arXiv).

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