• Zihao Yi, Qingxuan Jiang, Ruotian Ma, Xingyu Chen, Qu Yang, Mengru Wang, Fanghua Ye, Ying Shen, Zhaopeng Tu, Xiaolong Li, and Linus. Too good to be bad: On the failure of llms to role-play villains, 2025. https://arxiv.org/abs/2511.04962.
  • Huimu Yu, Xing Wu, Haotian Xu, Debing Zhang, and Songlin Hu. Codepmp: Scalable preference model pretraining for large language model reasoning. arXiv preprint arXiv:2410.02229, 2024.
  • Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. Hellaswag: Can a machine really finish your sentence? arXiv preprint arXiv:1905.07830, 2019.
  • Lunjun Zhang, Arian Hosseini, Hritik Bansal, Mehran Kazemi, Aviral Kumar, and Rishabh Agarwal. Generative verifiers: Reward modeling as next-token prediction. arXiv preprint arXiv:2408.15240, 2024a.
  • Xiaoying Zhang, Baolin Peng, Ye Tian, Jingyan Zhou, Lifeng Jin, Linfeng Song, Haitao Mi, and Helen Meng. Self- alignment for factuality: Mitigating hallucinations in llms via self-evaluation. arXiv preprint arXiv:2402.09267, 2024b.
  • Andy Zou, Zifan Wang, Nicholas Carlini, Milad Nasr, J Zico Kolter, and Matt Fredrikson. Universal and transferable adversarial attacks on aligned language models. arXiv preprint arXiv:2307.15043, 2023.
19