Practical Solutions for Assessing Privacy Norms Encoded in Large Language Models (LLMs)
Challenges in Evaluating LLMs
Large language models (LLMs) often encode societal norms from training data, raising concerns about privacy and ethical behavior. Ensuring these models adhere to societal norms across different contexts is crucial to prevent ethical issues.
Traditional Evaluation Limitations
Traditional methods focus on technical capabilities and neglect the encoding of societal norms. They fail to account for prompt sensitivity and variations in model hyperparameters, resulting in incomplete evaluations.
Introduction of LLM-CI Framework
A team of researchers introduces LLM-CI, a novel framework grounded in Contextual Integrity theory, to assess how LLMs encode privacy norms across different contexts. It employs a multi-prompt assessment strategy to provide a more accurate evaluation of norm adherence across models and datasets.
Testing and Results
LLM-CI was tested on datasets simulating real-world privacy scenarios, demonstrating a marked improvement in evaluating how LLMs encode privacy norms. Models optimized using alignment techniques showed up to 92% contextual accuracy in adhering to privacy norms.
Advancements and Value
LLM-CI offers a comprehensive and robust approach for assessing how LLMs encode privacy norms, significantly advancing the understanding of how well LLMs align with societal norms, particularly in sensitive areas such as privacy.
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