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

arXiv:2606.00467v2 Announce Type: replace-cross Abstract: 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 instructio