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Yijun Niu

2 accepted papers

2025

Adversarial Preference Learning for Robust LLM Alignment

ACL 2025finding

Modern language models often rely on Reinforcement Learning from Human Feedback (RLHF) to encourage safe behaviors. However, they remain vulnerable to adversarial attacks due to three key limitations: (1) the inefficiency and high cost of human annotation, (2) the vast diversity of potential adversa…

2025

CARE-STaR: Constraint-aware Self-taught Reasoner

ACL 2025finding

In real-world applications, large language models (LLMs) often need to handle diverse and complex instructions. Specifically, when instructions are subject to multiple constraints, some of which are somewhat ambiguous, LLMs often fail to produce answers that satisfy all constraints, limiting their e…