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New CIDER dataset aids LLMs in aligning with user privacy preferences

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced CIDER, a new dataset designed to help large language models better align with individual privacy preferences. The dataset contains over 14,000 human annotations across various communication scenarios, detailing users' willingness to share personal information. Experiments show that models like GPT-5.4 and Claude Sonnet 4.6 can improve prediction accuracy by up to 11.41 percentage points when personalized with contextual information, though this can sometimes lead to imbalanced error rates. AI

IMPACT This dataset could lead to LLMs that better respect user privacy in real-world interactions.

RANK_REASON The cluster describes a new dataset and research paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New CIDER dataset aids LLMs in aligning with user privacy preferences

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The cluster describes a new dataset and research paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Bingcan Guo, Eryue Xu, Jijie Zhou, Zhiping Zhang, Tianshi Li ·

    CIDER: A Dataset of Contextual Disclosure Boundaries for Privacy Preference Alignment

    arXiv:2608.09164v1 Announce Type: new Abstract: Aligning large language models (LLMs) with human privacy preferences requires capturing individuals' disclosure boundaries beyond general privacy norms. However, a gap remains in eliciting such nuanced preferences to evaluate alignm…

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