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 six toxicity datasets (five prima...
| Authors | Etienne Casanova, Rafal Kocielnik, R. Michael Alvarez |
|---|---|
| Primary category | cs.CL |
| Published | 30 May 2026 |
| Last updated at source | 2 October 2026 |
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