LLM-CI: A new machine learning framework for evaluating privacy standards encoded in LLM degrees

Long language models (LLMs) are widely deployed in sociotechnical systems such as healthcare and education. However, these models often encode social norms from the data used during training, raising concerns about their alignment with expectations of privacy and ethical behavior. The central challenge is to ensure that these models adhere to social norms across different contexts, model architectures, and datasets. Furthermore, cue sensitivity (where small changes in input cues lead to different responses) complicates the assessment of whether LLMs reliably encode these norms. Addressing this challenge is critical to preventing ethical issues such as unintended privacy violations in sensitive domains.

Traditional methods for evaluating LLMs focus on technical capabilities such as fluency and accuracy, and neglect the encoding of social norms. Some approaches attempt to evaluate privacy norms using specific cues or datasets, but they often fail to account for cue sensitivity, leading to unreliable results. Furthermore, variations in model hyperparameters and optimization strategies (such as capacity, alignment, and quantization) are rarely considered, resulting in incomplete assessments of LLM behavior. These limitations leave a gap in assessing LLMs’ ethical alignment with social norms.

A team of researchers from York University and the University of Waterloo introduces LLM-CI, a new framework based on contextual integrity (CI) theory, to assess how LLMs encode privacy norms in different contexts. It employs a multi-cue evaluation strategy to mitigate cue sensitivity by selecting cues that produce consistent results across multiple variants. This provides a more accurate assessment of norm adherence across models and datasets. The approach also incorporates real-world vignettes representing privacy-sensitive situations, ensuring a thorough assessment of model behavior in diverse scenarios. This method is a significant advance in assessing the ethical performance of LLMs, particularly in terms of privacy and social norms.

LLM-CI was tested on datasets such as IoT vignettes and COPPA vignettes, which simulate real-world privacy scenarios. These datasets were used to evaluate how the models handle contextual factors such as user roles and information types in various privacy-sensitive contexts. The evaluation also examined the influence of hyperparameters (e.g., model capacity) and optimization techniques (e.g., alignment and quantization) on adherence to the standard. The multi-cue methodology ensured that only consistent results were considered in the evaluation, which minimized the effect of cue sensitivity and improved the robustness of the analysis.

The LLM-CI framework demonstrated a marked improvement in assessing how LLMs encode privacy norms across different contexts. By applying the multi-cue assessment strategy, more consistent and reliable results were achieved than with single-cue methods. Models optimized using alignment techniques showed up to 92% contextual accuracy in privacy norm adherence. Furthermore, the new assessment approach resulted in a 15% increase in response consistency, confirming that fine-tuning model properties such as capacity and applying alignment strategies significantly improved LLMs’ ability to align with societal expectations. This validated the robustness of LLM-CI in norm adherence assessments.

LLM-CI offers a comprehensive and robust approach to assessing how LLMs encode privacy norms by leveraging a multi-cue evaluation methodology. It provides a reliable assessment of model behavior across different datasets and contexts, addressing the challenge of cue sensitivity. This method significantly advances the understanding of how well LLMs align with social norms, particularly in sensitive areas such as privacy. By improving the accuracy and consistency of model responses, LLM-CI represents a vital step toward ethically implementing LLMs in real-world applications.

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Aswin AK is a Consulting Intern at MarkTechPost. He is pursuing his dual degree from Indian Institute of technology, Kharagpur. He is passionate about Data Science and Machine Learning and has a strong academic background and hands-on experience in solving real-world interdisciplinary challenges.