On the Limits of LLM Adaptability: Impact of Model-Internalized Priors on Annotation Task Performance
arXiv:2606.00467v1 Announce Type: new 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 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 definitions. Through experiments on toxicity detecti
The rapid deployment of LLMs for automation and decision-making necessitates a deeper understanding of their reliability and biases.
This research highlights critical limitations in LLM adaptability and their internal biases, which directly impacts the accuracy and trustworthiness of AI systems deployed across industries.
Our understanding of LLMs' robustness to differing instructions and their susceptibility to misaligned task definitions is enhanced, informing better deployment strategies and development priorities.
- · AI safety researchers
- · Developers of robust LLM evaluation frameworks
- · Enterprises prioritizing reliable AI deployments
- · Companies relying on naive zero-shot LLM deployments
- · LLM providers with less transparent model mechanisms
Increased focus on robust prompting strategies and fine-tuning methods to mitigate LLM biases and 'decision stickiness'.
Development of new LLM architectures or training paradigms that explicitly account for and allow control over model-internalized priors.
Potential for regulatory discussions around transparency and explainability of LLM-driven decisions, especially in critical applications like legal or medical annotation.
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