Papers by Hongyan Chang

2 papers
Context-Aware Membership Inference Attacks against Pre-trained Large Language Models (2025.emnlp-main)

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Challenge: Prior Membership Inference Attacks on pre-trained Large Language Models fail at LLMs due to ignoring the generative nature of LLM data.
Approach: They propose a method that adapts MIA statistical tests to the perplexity dynamics of subsequences within a data point.
Outcome: The proposed method significantly outperforms prior approaches, revealing context-dependent memorization patterns in pre-trained LLMs.
Watermark Smoothing Attacks against Language Models (2025.findings-emnlp)

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Challenge: Watermarking is a key technique for detecting AI-generated text.
Approach: They propose a method to selectively smooth watermarks by leveraging the relationship between the model’s confidence and detectability.
Outcome: The proposed method selectively smoothes watermark traces while preserving text quality.

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