AIBearisharXiv – CS AI · 7h ago7/10
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On the Limits of LLM Adaptability: Impact of Model-Internalized Priors on Annotation Task Performance
Researchers demonstrate that Large Language Models exhibit significant limitations in zero-shot annotation tasks, with only 34.8% of initial errors correctable through prompting. The study reveals that model-internalized priors and concept definitions strongly influence LLM performance more than text-level memorization, highlighting fundamental constraints in LLM adaptability for reliable AI-as-a-judge applications.