Papers by Yangxi Li

2 papers
Reliably Bounding False Positives: A Zero-Shot Machine-Generated Text Detection Framework via Multiscaled Conformal Prediction (2025.acl-long)

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Challenge: Existing methods focus excessively on detection accuracy, neglecting the societal risks posed by high false positive rates (FPRs).
Approach: They propose a Conformal Prediction framework that constrains the upper bound of false positive rates and introduces a real-time detection framework.
Outcome: The proposed framework reduces false positive rates and improves detection performance.
Dynamic Evaluation with Cognitive Reasoning for Multi-turn Safety of Large Language Models (2025.acl-long)

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Challenge: Existing safety evaluation methods rely on static assessments that use fixed harmful prompts or predefined prefixes as jailbreak templates.
Approach: They propose a dynamic evaluation framework for multi-turn safety assessment of LLMs based on cognitive theories to simulate real chatting process and scenario simulation and strategy decision to guide dynamic generation.
Outcome: The proposed framework has been applied to evaluate the safety of widely used LLMs.

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